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

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

Shivlal Mewada, Sita Sharan Gautam, Pradeep Sharma
Copyright: © 2020 |Volume: 11 |Issue: 3 |Pages: 18
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781799806875|DOI: 10.4018/IJISMD.2020070102
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MLA

Mewada, Shivlal, et al. "Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data." IJISMD vol.11, no.3 2020: pp.22-39. http://doi.org/10.4018/IJISMD.2020070102

APA

Mewada, S., Gautam, S. S., & Sharma, P. (2020). Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data. International Journal of Information System Modeling and Design (IJISMD), 11(3), 22-39. http://doi.org/10.4018/IJISMD.2020070102

Chicago

Mewada, Shivlal, Sita Sharan Gautam, and Pradeep Sharma. "Artificial Bee Colony-Based Approach for Privacy Preservation of Medical Data," International Journal of Information System Modeling and Design (IJISMD) 11, no.3: 22-39. http://doi.org/10.4018/IJISMD.2020070102

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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.

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