The Community for Technology Leaders
Green Image
Issue No. 12 - December (2011 vol. 60)
ISSN: 0018-9340
pp: 1802-1817
Alex X. Liu , Michigan State University, East Lansing
Fei Chen , Michigan State University, East Lansing
JeeHyun Hwang , North Carolina State University, Raleigh
Tao Xie , North Carolina State University, Raleigh
Most prior research on policies has focused on correctness. While correctness is an important issue, the adoption of policy-based computing may be limited if the resulting systems are not implemented efficiently and thus perform poorly. To increase the effectiveness and adoption of policy-based computing, in this paper, we propose fast policy evaluation algorithms that can be adapted to support various policy languages. In this paper, we focus on XACML policy evaluation because XACML has become the de facto standard for specifying access control policies, has been widely used on web servers, and is most complex among existing policy languages. We implemented our algorithms in a policy evaluation system called XEngine and conducted side-by-side comparison with Sun Policy Decision Point (PDP), the industrial standard for XACML policy evaluation. The results show that XEngine is orders of magnitude faster than Sun PDP. The performance difference grows almost linearly with the number of rules in an XACML policy. To our best knowledge, there is no prior work on improving XACML policy evaluation performance. This paper represents the first step in exploring this unknown space.
Web servers, XACML, policy evaluation, policy-based computing, access control, policy decision point.

J. Hwang, T. Xie, A. X. Liu and F. Chen, "Designing Fast and Scalable XACML Policy Evaluation Engines," in IEEE Transactions on Computers, vol. 60, no. , pp. 1802-1817, 2010.
81 ms
(Ver 3.3 (11022016))