href="#ulink_14671332-7b09-5626-a250-d1e520ed75e0">Figure 2.5 Concept of Cloud Security [7].
Figure 2.6 Securities in Multi-Cloud environments [7].
2.4 Privacy in Multi-Tenancy with Deep Learning Concept
There is a need for privacy in a Multi-Tenancy system because of the risk of low privacy policies and weak security for data when tenants work on multitenant applications. The organisation and tenant are not able to self-secure all data [12]. Using Deep Learning creates a concept to secure data, providing there is a secure and private environment for the tenant to accept the Multi-Tenant application and work on that system freely. This is the reason for weak privacy and security polices, a risk of data loss, a risk of hacking of data, and the wrong use of information, so it is very necessary to secure the entire task before starting work on a Multi-Tenant system. For security or privacy, the first step is maintaining the concept of a unique ID [13]. In this model, all tenants have an individual, unique ID for login. If the organisation is very large and it is complicated to manage all the IDs, then each individual department will have a single ID to login. This concept is also used for using Deep Learning. The second step is to make access limitations for each tenant. The access of each tenant is dependent on the organisation or authorised department deciding the access limitation. For example, a company whose tenants are working in an account department are able to access only the account department data, they are not able access other departments’ (admin, security, etc.) data. According to this concept, all department access criteria is decided or fixed and department tenant access limitation is decided so the authorisation is checked and only authorised users can access the limited data. The third step is to isolate the database into tables according to department. The database is then separated and isolated into tables according to tenant access and limitation-isolated data is provided to the tenant. From this method, the whole database is not given to all tenants and only isolated data is provided by using a Deep Learning concept shown in Figure 2.7.
Figure 2.7 Multi-Tenancy services [8].
The fourth step is encryption in a Multi-Tenant based system. In a Multi-Tenant based system, the consistency, integrity, durability, accuracy, and on-time demand of a database is mandatory for fulfilment. If the Multi-Tenant based system does not fulfil the requirements due to any term and condition, the tenant may not be able to work efficiently in the organisation, so encryption techniques based on Deep Learning concepts are used to secure the database. Some encryption techniques include digital, security, key, signature, digital key, private key, and password provided encryption [14].
The authorised user accesses the sophisticated database and can modify the database. If unauthorised access happens, sophisticated data access by the unauthorized user can be added, deleted, and modified by unauthorised activity. In a Multi-Tenant system using encryption techniques, first check the authorisation, find out if the tenant is authorised or not, and if the tenant has been authorised as a user with the access provided.
2.5 Related Work
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].
2.6 Conclusion
In Cloud Computing with a Multi-Tenancy system, privacy and security are very complicated and valuable. These concepts are important because it is a responsibility to provide privacy and security to the unique architecture of cloud computing and multi-tenant systems. The data must be correct, durable, and secure. Every tenant wants to work in a secure environment; this helps create a good and graceful environment for the work place. Every tenant wants security and privacy to be maintained for database transactions in the cloud environment. If this requirement is not fulfilled, the tenant will work for longer durations and the ability for work will reduce, so privacy and security are two factors which decide the future of that structure. Using the Deep Learning concept, we work on the privacy and security areas of Multi-Tenancy systems making them more secure for both physical and logical separation and also provide a great privacy platform to work free from any worry about security. With the help of Deep Learning, the Multi-Tenant system makes things more secure and privacy policies more stable to work with and secures the future safety of the database used by different tenants of the same organisation. Using the Deep Learning concept provides mechanisms to make privacy architecture to enhance the security level of privacy policies. Data is secure on the front and back ends, so the isolation of data is protected at both ends and is safe for future use by the tenant. It is sophisticated and necessary for the privacy and security of each face of a cloud based Multi-Tenant system to maintain no loss of data for the durability and safe side of a system.
References
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2. Bhargava, N., Bhargava, R., Rathore, P. S., & Kumar, A. (2020). Texture Recognition Using Gabor Filter for Extracting Feature Vectors With the Regression Mining Algorithm. International Journal of Risk and Contingency Management (IJRCM), 9(3), 31-44. doi:10.4018/IJRCM.2020070103
3. By Judith Hurwitz https://www.dummies.com/programming/cloud-computing/hybrid-cloud/multi-tenancy-and-its-benefits-in-a-saas-cloud-computing-environment/ by Judith Hurwitz, Marcia Kaufman, Fern Halper, Daniel Kirsch
