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Access control models are an important tool developed for securing today’s data systems. Institutions use the access control models specifically to define who their employees are, what they can do, which resources they can reach, and which processes they can perform and use them to manage the whole process. This is a very hard and costly process for institutions with distributed database systems. However, access control models cannot be implemented in a qualified way due to the fact that the conditions for defining users’ demands to reach resources distributed on different servers, one of which is consequentially bound to the other, the verification and authorization of those user demands, and being able to monitor the actions of the users cannot be configured in an efficient way all the time. With our model suggested in this study, the aim is to automatically calculate the permissions and access levels of all users defined in the distributed database systems for the objects, and, in this way, we will reach a more efficient decision as to which objects the users can access while preventing their access to the information they do not need. Our proposed model in this study has been applied to real life data clusters from organizations providing health and education services and a public service. With the proposed model, all models have been run on servers sharing resources in a private network. The performance of the proposed model has been compared to that of traditional access models. It was confirmed that the proposed model presented an access control model providing more accurate access level results as well as being scalable to many distributed database systems.

A New Scalable and Expandable Access Control Model for Distributed Database Systems in Data Security