Big Data and Machine Learning Based Secure Healthcare Framework

TLDR

The paper introduces big data’s growing role in healthcare, noting its use in managing rapid data growth and the complex potential of machine learning to transform the industry. The study first empirically examines big data’s impact on healthcare and then proposes a novel smart, secure information system that leverages machine learning and advanced security mechanisms to handle medical big data. The proposed system incorporates optimal storage and a multi‑layer security architecture, employing masking encryption, activity monitoring, granular access control, dynamic data encryption, and endpoint validation to safeguard medical big data. Significant work has been done using big data in healthcare, yet many studies overlook privacy and security, and the proposed hybrid four‑layer model appears to be a more effective disease‑diagnostic big‑data system.

Abstract

The paper presents a brief introduction to big data and its role in healthcare applications. It is observed that the use of big data architecture and techniques are continuously assisting in managing the expeditious data growth in healthcare industry. Here, initially an empirical study is performed to analyze the role of big data in healthcare industry. It has been observed that significant work has been done using big data in healthcare sector. Nowadays, it is intricate to envision the way the machine learning and big data can influence the healthcare industries. It has been observed that most of the authors who implemented the use of machine learning and big data analytics in disease diagnosis have not given significant weightage to the privacy and security of the data. Here, a novel design of smart and secure healthcare information system using machine learning and advanced security mechanism has been proposed to handle big data of medical industry. The innovation lies in the incorporation of optimal storage and data security layer used to maintain security and privacy. Different techniques like masking encryption, activity monitoring, granular access control, dynamic data encryption and end point validation have been incorporated. The proposed hybrid four layer healthcare model seems to be more effective disease diagnostic big data system.