IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0248737.html
   My bibliography  Save this article

Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function

Author

Listed:
  • Yaling Zhang
  • Jin Han

Abstract

Fuzzy C-means clustering algorithm is one of the typical clustering algorithms in data mining applications. However, due to the sensitive information in the dataset, there is a risk of user privacy being leaked during the clustering process. The fuzzy C-means clustering of differential privacy protection can protect the user’s individual privacy while mining data rules, however, the decline in availability caused by data disturbances is a common problem of these algorithms. Aiming at the problem that the algorithm accuracy is reduced by randomly initializing the membership matrix of fuzzy C-means, in this paper, the maximum distance method is firstly used to determine the initial center point. Then, the gaussian value of the cluster center point is used to calculate the privacy budget allocation ratio. Additionally, Laplace noise is added to complete differential privacy protection. The experimental results demonstrate that the clustering accuracy and effectiveness of the proposed algorithm are higher than baselines under the same privacy protection intensity.

Suggested Citation

  • Yaling Zhang & Jin Han, 2021. "Differential privacy fuzzy C-means clustering algorithm based on gaussian kernel function," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-20, March.
    • Handle: RePEc:plo:pone00:0248737
    DOI: 10.1371/journal.pone.0248737
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0248737
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0248737&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0248737?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yaling Zhang & Na Liu & Shangping Wang, 2018. "A differential privacy protecting K-means clustering algorithm based on contour coefficients," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sadia Basar & Mushtaq Ali & Gilberto Ochoa-Ruiz & Mahdi Zareei & Abdul Waheed & Awais Adnan, 2020. "Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.
    2. Hossam M J Mustafa & Masri Ayob & Mohd Zakree Ahmad Nazri & Graham Kendall, 2019. "An improved adaptive memetic differential evolution optimization algorithms for data clustering problems," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-28, May.
    3. Zhao, Laijun & Li, Deqiang & Guo, Xiaopeng & Xue, Jian & Wang, Chenchen & Sun, Wenjun, 2021. "Cooperation risk of oil and gas resources between China and the countries along the Belt and Road," Energy, Elsevier, vol. 227(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0248737. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.