A survey on securing and optimizing health care Bigdata

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 multipurpose 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.