A Review: Data Security in Cloud Computing Using Machine Learning

Abstract

In today's IT industry, cloud computing, and the broader cloud services sector have gained widespread acceptance. To put it simply, cloud services provide cloud computing, which enables on-demand access to IT resources through the internet and employs a pay-per-use pricing model based on subscription authorization and centralized hosting. The need for public cloud services is increasing at an unprecedented rate as the 21st century becomes more digital. Cloud computing makes it easy to access servers, storage, databases, and various application services via the internet. The cloud environment supports a variety of advantages, but it also has a few drawbacks. Regardless of the highest industry certifications and security requirements implemented by cloud service providers, there are always risks when storing sensitive data on third-party service providers. Security and privacy must play a significant role when discussing data security, especially when handling sensitive data. Many solutions have been created to deal with this problem. It is necessary to discover, classify, and analyze the significant existing work because there is a shortage of analysis among the current solutions. This paper relates and briefly analyses the top methods for safely exchanging and safeguarding data in a cloud setting. The discussion of each specific technique covers its role in data protection, prospective and reliable solutions in the field, scope, future directions, etc. The applicability and integrity of the methodologies are then explored regarding the demands and results.