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Multi Attribute Case Based Privacy-preserving for Healthcare Transactional Data Using Cryptography

K. Saranya*, K. Premalatha

Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, 638401, India

* Corresponding Author: K. Saranya. Email: email

Intelligent Automation & Soft Computing 2023, 35(2), 2029-2042. https://doi.org/10.32604/iasc.2023.027949

Abstract

Medical data mining has become an essential task in healthcare sector to secure the personal and medical data of patients using privacy policy. In this background, several authentication and accessibility issues emerge with an intention to protect the sensitive details of the patients over getting published in open domain. To solve this problem, Multi Attribute Case based Privacy Preservation (MACPP) technique is proposed in this study to enhance the security of privacy-preserving data. Private information can be any attribute information which is categorized as sensitive logs in a patient’s records. The semantic relation between transactional patient records and access rights is estimated based on the mean average value to distinguish sensitive and non-sensitive information. In addition to this, crypto hidden policy is also applied here to encrypt the sensitive data through symmetric standard key log verification that protects the personalized sensitive information. Further, linear integrity verification provides authentication rights to verify the data, improves the performance of privacy preserving technique against intruders and assures high security in healthcare setting.

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Cite This Article

K. Saranya and K. Premalatha, "Multi attribute case based privacy-preserving for healthcare transactional data using cryptography," Intelligent Automation & Soft Computing, vol. 35, no.2, pp. 2029–2042, 2023. https://doi.org/10.32604/iasc.2023.027949



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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