IDEAS home Printed from https://ideas.repec.org/a/igg/jismd0/v11y2020i3p22-39.html
   My bibliography  Save this article

Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data

Author

Listed:
  • Shivlal Mewada

    (Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, India)

  • Sita Sharan Gautam

    (Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, India)

  • Pradeep Sharma

    (Government Model Autonomous Holkar Science College, India)

Abstract

A large amount of data is generated through healthcare applications and medical equipment. This data is transferred from one piece of equipment to another and sometimes also communicated over a global network. Hence, security and privacy preserving are major concerns in the healthcare sector. It is seen that traditional anonymization algorithms are viable for sanitization process, but not for restoration task. In this work, artificial bee colony-based privacy preserving model is developed to address the aforementioned issues. In the proposed model, ABC-based algorithm is adopted to generate the optimal key for sanitization of sensitive information. The effectiveness of the proposed model is tested through restoration analysis. Furthermore, several popular attacks are also considered for evaluating the performance of the proposed privacy preserving model. Simulation results of the proposed model are compared with some popular existing privacy preserving models. It is observed that the proposed model is capable of preserving the sensitive information in an efficient manner.

Suggested Citation

  • Shivlal Mewada & Sita Sharan Gautam & Pradeep Sharma, 2020. "Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 11(3), pages 22-39, July.
  • Handle: RePEc:igg:jismd0:v:11:y:2020:i:3:p:22-39
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISMD.2020070102
    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:jismd0:v:11:y:2020:i:3:p:22-39. 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.