Читать книгу Deep Learning Approaches to Cloud Security - Группа авторов - Страница 38

In this chapter, we will look at the work related to the concept of Multi-Tenancy privacy policies. Future use of Multi-Tenancy in the cloud environment is dependent on the complexity and cost affected to the data structure. There are many works done in many chapter basics in privacy and security concepts [15] where database hacking and transition fraud happened. The use of Deep Learning removes that type of problem and reduces fraud. This chapter data is useful to find out the functional or non-functional parameters of clouding computing systems with respect to Multi-Tenant systems. These details take discretion from parameters like security and privacy concepts, detail descriptions on the structure of Multi-Tenancy in cloud based frameworks, vary modules of Multi-Tenancy use according to requirements, and discuss the security, privacy, performance, cost, and flexibility factors of Multi-Tenancy cloud based systems. This chapter also discusses the contributions of Deep Learning concepts used in data security and privacy and in protection concepts as cloud computing system architecture. This chapter is used to find the maximum solution to protect and maintain the privacy and security of databases and the work place of tenants in Multi-Tenancy based systems using Deep Learning concepts and understand the structure of cloud computing and deep structure of Multi-Tenancy with a privacy concept of Deep Learning methods. This literature is used to understand and find the requirements of resources, services, and privacy concept development in various services like response time, network load, and throughput management services development, as well as the need for resources and requirement of resources in a cloud based Multi-Tenant system and privacy services using Deep Learning concepts [16].