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.