PRESERVING HUMAN HEALTH CARE RECORDS BASED ON HYBRID LEARNING METHODS IN CLOUD COMPUTING - et al., Sonya A (International Journal of Control and Automation, 2019)
In existing cryptographic algorithms, can be use for boundless security reason, however they don’t satisfy transfer speed, memory space and CPU usage according to cloud systems requirements. In addition, they exhibit high IP spoofing and side channel hackers. Hacking rate has been increased in numerous area applications like Health Care, Banking and Military application. Healthcare is one of an emerging field in health industry. So as to analyze a patient, specialists need the restorative record of the patient which contains sight and sound huge information. Leakage of sensitive e-health information would seriously motivation dangers prompting altering of medical health care reports and individual related data. So protecting the security of the patient information is a fundamental element in healthcare systems. With the progression in security approaches especially in human services area, enormous measure of information is gathered from various sensors and this information are moved and put away in cloud computing. It will be hard to deal with large quantity of data in healthcare area where it requires day by day patients information processing and memory space. The information gathered from it is being encoded based on Hybrid learning algorithm and block chain technology before send it to in cloud storage. This paper proposed a modified security learning algorithm and to make them suitable for Real Time applications. Simulation and analysis of the proposed algorithm showed that its bandwidth, memory space and CPU utilization are increased as compared to traditional mechanism with less power, cost and time consumption.