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Differentially Private Clustering Algorithm for Mixed Data

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Ubiquitous Security (UbiSec 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1768))

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Abstract

Inspired by the current practice where mixed data is the norm instead of exceptions and the privacy concerns on data management, we propose a differentially private mixed data clustering (DPMC) algorithm considering the cluster analysis on both numerical and categorical data. First, we design an adaptive privacy budget allocation method to analyze the loss due to added noise, thus determining the number of iterations and the privacy budget given accuracy and dataset characteristics. Next, we develop an optimization method based on consistency inference for categorical attributes, in order to improve the clustering performance. Finally, comparative experiments have been carried out using four real-world datasets. The results demonstrate significant improvement in balancing between privacy protection and performance.

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Acknowledgments

This work is supported by the science and technology project of State Grid Corporation of China entitled: "Research on Power Marketing Data Sharing and Model Fusion Technology Based on Federated Learning" (Grant No. 5700-202113262A-0–0-00).

Author information

Authors and Affiliations

  1. State Grid Hebei Information and Telecommunication Branch, Shijiazhuang, 050021, China

    Kai Cheng, Liandong Chen & Huifeng Yang

  2. School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, China

    Dan Luo & Zhitao Guan

  3. Department of Finance, Operations, and Information Systems, Brock University, Ontario, Canada

    Shuai Yuan

Authors
  1. Kai Cheng
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  2. Liandong Chen
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  3. Huifeng Yang
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  4. Dan Luo
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  5. Shuai Yuan
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  6. Zhitao Guan
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Corresponding author

Correspondence to Zhitao Guan .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. University of Texas at San Antonio, San Antonio, TX, USA

    Kim-Kwang Raymond Choo

  3. Temple University, Philadelphia, PA, USA

    Jie Wu

  4. Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates

    Ernesto Damiani

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Cheng, K., Chen, L., Yang, H., Luo, D., Yuan, S., Guan, Z. (2023). Differentially Private Clustering Algorithm for Mixed Data. In: Wang, G., Choo, KK.R., Wu, J., Damiani, E. (eds) Ubiquitous Security. UbiSec 2022. Communications in Computer and Information Science, vol 1768. Springer, Singapore. https://doi.org/10.1007/978-981-99-0272-9_27

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  • DOI: https://doi.org/10.1007/978-981-99-0272-9_27

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-0271-2

  • Online ISBN: 978-981-99-0272-9

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