Name
Abstract | ||
|---|---|---|
Attribute Based Access Control (ABAC) is fast replacing traditional access control models due to its dynamic nature, flexibility and scalability. ABAC is often used in collaborative environments. However, a major hurdle to deploying ABAC is to precisely configure the ABAC policy. In this paper, we present an ABAC mining approach that can automatically discover the appropriate ABAC policy rules. We first show that the ABAC mining problem is equivalent to identifying a set of functional dependencies in relational databases that cover all of the records in a table. We also propose a more efficient algorithm, called ABAC-SRM which discovers the most general policy rules from a set of candidate rules. We experimentally show that ABAC-SRM is accurate and significantly more efficient than the existing state of the art. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CIC.2017.00051 | 2017 IEEE 3rd International Conference on Collaboration and Internet Computing (CIC) |
Keywords | Field | DocType |
ABAC Mining,Access Control,Policy Mining | Attribute-based access control,Computer vision,Relational database,Computer science,Top-down and bottom-up design,Authorization,Functional dependency,Access control,Artificial intelligence,Database,Scalability | Conference |
Volume | ISBN | Citations |
2017 | 978-1-5386-2566-8 | 2 |
PageRank | References | Authors |
0.40 | 18 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanay Talukdar | 1 | 2 | 1.08 |
| Gunjan Batra | 2 | 3 | 1.17 |
| Jaideep Vaidya | 3 | 2778 | 171.18 |
| Vijayalakshmi Atluri | 4 | 3256 | 424.98 |
| Shamik Sural | 5 | 1008 | 96.36 |