ABSTRACT
In the realm of healthcare, big data plays a crucial role by providing the necessary data for deep learning algorithms. This data, collected from various IoT devices, is stored in the cloud and used to improve healthcare services. The security of this sensitive health-related information is facilitated by big data security frameworks that automate security evaluation and analysis. Big data operations now use cloud deployment designs as their preferred computing model. This development was driven by the scalability, flexibility, and affordability of these solutions. These characteristics make it an extreme challenge for healthcare organisations using traditional data management mechanisms to store and process these huge datasets. Under a deployment approach like this, the user no longer maintains the data physically, which causes security issues. In this situation, the taking up of cloud architectures is majorly facilitated by big data security. Big data is based on an automated framework for security evaluation and an approach to security analysis. This framework makes the design phase, mapping of the big data, and cloud security best practices. As a result of this, the security design time is reduced, and the knowledge of security parameters is raised. The framework’s advantages and disadvantages are reviewed thoroughly, and the existing and ongoing problems in the big data and cloud-related field are highlighted. Working with big data in the cloud presents a unique difficulty of balancing two incompatible design tenets. Big data systems (like Hadoop) are founded on the share-nothing principle, where each node is independent and self-sufficient, but cloud computing is focused on the notions of consolidation and resource sharing. This chapter provides an overview of the big data idea; examines relevant mathematical and data analytics methods; and provides a taxonomy of the current tools, frameworks, and platforms for various big data computing models.
