A distributed PDP model based on spectral clustering for improving evaluation performance

• Published in: World wide web (Bussum) Vol. 22; no. 4; pp. 1555 - 1576
• Main Authors: Deng, Fan, Lu, Jie, Wang, Shi-Yu, Pan, Jie, Zhang, Li-Yong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2019; Springer Nature B.V
• Subjects: Access control; Architecture; Clustering; Computer Science; Control systems design; Database Management; Information Systems Applications (incl.Internet); Operating Systems; Security management; Systems analysis; Clustering and reordering; Spectral clustering; Evaluation performance; Distributed deployment; Policy decision point(PDP)
• ISSN: 1386-145X; 1573-1413
• DOI: 10.1007/s11280-018-0588-8

In modern access control systems, the Policy Decision Point (PDP) needs to be more efficient to meet the ever-growing demands of Web access authorization. Present XACML implementations of access control systems follow the same architecture based on ABAC, but varies in the design of PDP and other components. As a critical process in PDP, evaluation of attributes is often implemented in a simple and inefficient way in real applications. In order to improve the PDP evaluation performance, we propose a novel distributed PDP model, called XPDP, based on the combination of two-stage clustering and reordering to eliminate the limitation of computational performance of a single PDP. Firstly, we cluster rules based on subject and use spectral clustering method to perform further clustering. Secondly, the clusters of rules are reordered before evaluation for every inbound request based on similarity. Finally, we introduce a distributed PDP architecture for distributed deployment, providing with a brand new perspective of designing access control systems. A comparison in evaluation performance between the XPDP and the Sun PDP, as well as SBA-XACML, is made. In the experiment of using 10,000 synthetic access requests with three practical policy sets, the XPDP is 3.26 times faster than Sun PDP, and is 1.85 times faster than SBA-XACML. Experimental results show that the PDP evaluation performance can be prominently improved .