Novel Weight-Improved Particle Swarm Optimization to Enhance Data Security in Cloud

The rapid growth of cloud computing has brought numerous benefits and opportunities, but it has raised concerns about data security. To protect sensitive data stored in the cloud is crucial and to ensure data privacy, confidentiality, and integrity for cloud users. The cloud as a platform enables real-time patient monitoring via the use of sensors, which has the potential to revolutionize the healthcare industry. Users transmit sensitive and classified medical information to cloud service providers for centralized storage and processing. This presents cybercriminals with the opportunity to acquire data, interfere with ongoing data processes, and restrict healthcare professionals and patients from accessing confidential information. For a healthcare organization to feel comfortable with and use the cloud computing platform, privacy and security are the two most pressing concerns. Fine-grained access control (FGAC) is used as a tool for controlling who may see what data in a cloud-based healthcare setting. Protects private medical information by limiting who may see it, change it, or even access it at all. Over the last several years, advances in machine learning have made cloud data security one of the most promising areas in the field. In the proposed system the Weight-Improved Particle Swarm Optimization Algorithm (WI-PSO) and machine learning classifiers are implemented to enhance data security in the cloud.