Incremental Maintenance of ABAC Policies

Area of Science:

• Computer Science
• Information Security
• Data Mining

Background:

• Attribute Based Access Control (ABAC) policy discovery is crucial for managing access permissions.
• Existing ABAC mining methods assume static data and require re-execution for policy updates, leading to inefficiency.
• Real-world access policies are dynamic and subject to frequent changes.

Purpose of the Study:

• To propose an incremental approach for maintaining Attribute Based Access Control (ABAC) policies.
• To address the inefficiency of re-mining ABAC policies with every change in access permissions or attributes.
• To develop a method that updates only affected rules in dynamic ABAC environments.

Main Methods:

• Developed an incremental algorithm for updating ABAC policies.
• Focused on updating only the rules impacted by changes in access permissions or attributes.
• Conducted a comprehensive experimental evaluation to compare performance.

Main Results:

• The proposed incremental approach significantly enhances efficiency compared to conventional ABAC mining.
• The method effectively handles dynamic changes in access control policies.
• Experimental results validate the superior performance of the incremental maintenance strategy.

Conclusions:

• Incremental maintenance is a more efficient strategy for dynamic ABAC policies.
• The proposed method offers a practical solution for real-world, evolving access control systems.
• Further research can explore optimizations for complex, large-scale ABAC environments.