A Survey on Securing and Optimizing Health Care Bigdata

  • Authors

    • K. Kavitha
    • D. Anuradha
    • P. Pandian
    https://doi.org/10.14419/ijet.v7i4.10.21212

    Received date: October 7, 2018

    Accepted date: October 7, 2018

    Published date: October 2, 2018

  • Big-data, Healthcare, Information security, Optimization, Multi-objective optimization, Simulation.

  • Abstract

    Huge amount of health care data are available online to improve the overall performance of health care system. Since this huge health care Big-data is valuable and sensitive, it requires safety. In this paper we analyze numerous ways in which the health care Big-data can be protected. In recent days many augmented security algorithm that are suitable for Big-data have emerged like, El-Gamal, Triple-DES, and Homomorphic algorithms. Also authentication and access control can be implemented over Big-data using Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) schemes.

    Along with security to Big-data we try to evolve the ways in which the valuable Big-data can be optimized to improve the Big-data analysis. Mathematical optimization techniques such as simple and multi-purpose optimization and simulation are employed in Big-data to maximize the patient satisfaction and usage of doctor’s consulting facility. And also, to minimize the cost spent by patient and energy wasted.

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  • How to Cite

    Kavitha, K., Anuradha, D., & Pandian, P. (2018). A Survey on Securing and Optimizing Health Care Bigdata. International Journal of Engineering and Technology, 7(4.10), 504-507. https://doi.org/10.14419/ijet.v7i4.10.21212