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An Efficient Private Evaluation of a Decision Graph

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Information Security and Cryptology – ICISC 2018 (ICISC 2018)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11396))

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Abstract

A decision graph is a well-studied classifier and has been used to solve many real-world problems. We assumed a typical scenario between two parties in this study, in which one holds a decision graph and the other wants to know the class label of his/her query without disclosing the graph and query to the other. We propose a novel protocol for this scenario that can obliviously evaluate a graph that is designed by an efficient data structure called the graph level order unary degree sequence (GLOUDS). The time and communication complexities of this protocol are linear to the number of nodes in the graph and do not include any exponential factors. The experiment results revealed that the actual runtime and communication size were well concordant with theoretical complexities. Our method can process a graph with approximately 500 nodes in only 11 s on a standard laptop computer. We also compared the runtime of our method with that of previous methods and confirmed that it was one order of magnitude faster than the previous methods.

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Acknowledgements

A part of this work is supported by Okawa Foundation Research Grant and JST CREST grant numbers: JPMJCR1503, JPMJCR1688, JPMJCR14D6.

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Authors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroki Sudo & Kana Shimizu

  2. AIST-Waseda University CBBD-OIL, Tokyo, Japan

    Hiroki Sudo

  3. The University of Tokyo, Tokyo, Japan

    Koji Nuida

Authors
  1. Hiroki Sudo
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  2. Koji Nuida
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  3. Kana Shimizu
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Corresponding author

Correspondence to Kana Shimizu .

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Editors and Affiliations

  1. Sejong University, Seoul, Korea (Republic of)

    Kwangsu Lee

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Sudo, H., Nuida, K., Shimizu, K. (2019). An Efficient Private Evaluation of a Decision Graph. In: Lee, K. (eds) Information Security and Cryptology – ICISC 2018. ICISC 2018. Lecture Notes in Computer Science(), vol 11396. Springer, Cham. https://doi.org/10.1007/978-3-030-12146-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-12146-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12145-7

  • Online ISBN: 978-3-030-12146-4

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