Читать книгу Deep Learning Approaches to Cloud Security - Группа авторов - Страница 37

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.