Document Type
Article
Publication Date
1-1-2020
Publication Title
Journal of Computer Science
Publisher
Science Publications
Volume
16
Issue
1
First page number:
72
Last page number:
91
Abstract
Among the access control methods for database security, there is Mandatory Access Control (MAC) model in which the security level is set to both the subject and the object to enhance the security control. Legacy MAC models have focused only on one thing, either confidentiality or integrity. Thus, it can cause collisions between security policies in supporting confidentiality and integrity simultaneously. In addition, they do not provide a granular security class policy of subjects and objects in terms of subjects' roles or tasks. In this paper, we present the security policy of Bell_LaPadula Model (BLP) model and Biba model as one complemented policy. In addition, Duties Separation and Data Coloring (DSDC)-MAC model applying new data coloring security method is proposed to enable granular access control from the viewpoint of Segregation of Duty (SoD). The case study demonstrated that the proposed modeling work maintains the practicality through the design of Human Resources management System. The proposed model in this study is suitable for organizations like military forces or intelligence agencies where confidential information should be carefully handled. Furthermore, this model is expected to protect systems against malicious insiders and improve the confidentiality and integrity of data.
Keywords
Mandatory Access Control (MAC); SoD-driven access control; Data coloring access control; Security key authorization; Complemented BLP and Biba Model
Disciplines
Computer Sciences
File Format
File Size
1.009 KB
Language
English
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Repository Citation
Lee, S.,
Kim, Y.,
Kim, J.,
Song, C.
(2020).
A Design of MAC Model Based on the Separation of Duties and Data Coloring: DSDC-MAC.
Journal of Computer Science, 16(1),
72-91.
Science Publications.
http://dx.doi.org/10.3844/jcssp.2020.72.91