What is it about?
Imagine your social media account. You have rules for who can see your photos: your "friends" can, but the public can't. This is a simple form of access control where access to information is determined by the relationships between people and data, like "colleague," "family," or "manager." In the literature, these systems work with "PERMIT" rules. If a "friend" relationship exists, access is granted. If no such rule exists, access is automatically denied. This works for simple cases, but what about exceptions? To handle these complex scenarios, a system needs the power not only to grant permission but also to enforce explicit prohibitions.
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Why is it important?
This work is significant because it makes policies more concise, and more realistic. - It’s More Concise: This is the first method to discover both "PERMIT" and "DENY" rules for relationship-based systems. This allows policies to handle specific exceptions that are common in the real world but can't be captured by "PERMIT" rules alone. - It’s Proven to be Necessary: In a majority of complex scenarios (about 77% of test cases), it is impossible to create an accurate policy without using explicit "DENY" rules. - Creates More Manageable Policies: The method is designed to find the smallest possible set of rules needed to do the job. For system administrators, having a concise and minimal rulebook makes the policy easier to understand, maintain, and audit.
Perspectives
As a researcher, examining a critical problem in the scientific literature is always a rewarding experience. It was fun to work on this challenge and try to find a solution for a significant gap. The process of deeply understanding the problem, giving it a formal structure, and then piecing together a solution felt like solving an intricate puzzle with important, real-world consequences.
Ferhat Demirkiran
University at Albany State University of New York
Read the Original
This page is a summary of: Enhancing Relationship-Based Access Control Policies with Negative Rule Mining, June 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3714393.3726510.
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