Analyzing the big data security through a unified decision-making approach

The use of cloud services, web-based software systems, the Internet of Things (IoT), Machine Learning (ML), Artificial Intelligence (AI), and other wireless sensor devices in the health sector has resulted in significant advancements and benefits. Early disease detection, increased accessibility, and high diagnostic reach have all been made possible by digital healthcare. Despite this remarkable achievement, healthcare data protection has become a serious issue for all parties involved. According to data breach statistics, the healthcare data industry is one of the major threats to cyber criminals. In reality, healthcare data breaches have increased at an alarming rate in recent years. Practitioners are developing a variety of tools, strategies, and approaches to solve healthcare data security concerns. The author has highlighted the crucial measurements and parameters in relation to enormous organizational circumstances for securing a vast amount of data in this paper. Security measures are those that prevent developers and organizations from achieving their objectives. The goal of this work is to identify and prioritize the security approaches that are used to locate and solve problems using different versions of two approaches that have been used to analyze big data security in the past. The Fuzzy Analytic Hierarchy Process (Fuzzy AHP) approach is being used by authors to examine the priorities and overall data security. In addition, the most important features in terms of weight have been quantitatively analyzed. Experts will discover the findings and conclusions useful in improving big data security.