IDEAS home Printed from https://ideas.repec.org/a/igg/jdwm00/v19y2023i5p1-16.html
   My bibliography  Save this article

Cybersecurity of Medical Data Based on Big Data and Privacy Protection Method

Author

Listed:
  • Jianhong Li

    (Second Affiliated Hospital of Wenzhou Medical University, China)

  • An Pan

    (Second Affiliated Hospital of Wenzhou Medical University, China)

  • Tongxing Zheng

    (Second Affiliated Hospital of Wenzhou Medical University, China)

Abstract

Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.

Suggested Citation

  • Jianhong Li & An Pan & Tongxing Zheng, 2023. "Cybersecurity of Medical Data Based on Big Data and Privacy Protection Method," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 19(5), pages 1-16, April.
  • Handle: RePEc:igg:jdwm00:v:19:y:2023:i:5:p:1-16
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDWM.325222
    Download Restriction: no
    ---><---

    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:igg:jdwm00:v:19:y:2023:i:5:p:1-16. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.