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A High-Availability K-modes Clustering Method Based on Differential Privacy

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Algorithms and Architectures for Parallel Processing (ICA3PP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 13156))

Abstract

In categorical data mining, the K-modes algorithm is a classic algorithm that has been widely used. However, the data analyzed by the K-modes algorithm usually contains sensitive user information. If these data are leaked, it will seriously threaten the privacy of users. In response to this problem, the existing method that combines differential privacy with the K-modes algorithm can effectively prevent privacy leakage. Nevertheless, differential privacy adds noise to the data while protecting data privacy, which will reduce the availability of clustering results. In this paper, we propose a high-availability K-modes clustering mechanism based on differential privacy(HAKC). In this mechanism, based on the use of differential privacy to protect data privacy, we select the initial centroid of the clustering by calculation, and improve the calculation method of the distance between the data point and the centroid in the iterative process.

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Acknowledgments

This work was supported in part by the Hunan Provincial Education Department of China under Grant number 21A0318, and the Research project on Teaching Reform of Ordinary Colleges and Universities in Hunan Province under Grant Number HNJG-2021-0651.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Shaobo Zhang, Liujie Yuan, Yuxing Li, Wenli Chen & Yifei Ding

  2. Hunan Key Laboratory of Service Computing and New Software Service Technology, Xiangtan, 411201, China

    Shaobo Zhang, Liujie Yuan, Yuxing Li, Wenli Chen & Yifei Ding

  3. Key Laboratory of Software Engineering for Complex Systems, College of Computer, National University of Defense Technology, Changsha, 410073, China

    Shaobo Zhang

Authors
  1. Shaobo Zhang
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  2. Liujie Yuan
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  3. Yuxing Li
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  4. Wenli Chen
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  5. Yifei Ding
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Corresponding author

Correspondence to Shaobo Zhang .

Editor information

Editors and Affiliations

  1. Xiamen University, Xiamen, China

    Yongxuan Lai

  2. Beijing Normal University, Zhuhai, China

    Tian Wang

  3. Xiamen University, Xiamen, China

    Min Jiang

  4. Tianjin University, Tianjin, China

    Guangquan Xu

  5. Hunan University, Changsha, China

    Wei Liang

  6. University of Naples Parthenope, Naples, Italy

    Aniello Castiglione

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Zhang, S., Yuan, L., Li, Y., Chen, W., Ding, Y. (2022). A High-Availability K-modes Clustering Method Based on Differential Privacy. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13156. Springer, Cham. https://doi.org/10.1007/978-3-030-95388-1_18

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  • DOI: https://doi.org/10.1007/978-3-030-95388-1_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95387-4

  • Online ISBN: 978-3-030-95388-1

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