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Policy Decomposition for Evaluation Performance Improvement of PDP

Author

Listed:
  • Fan Deng
  • Ping Chen
  • Li-Yong Zhang
  • Xian-Qing Wang
  • Sun-De Li
  • Hui Xu

Abstract

In conventional centralized authorization models, the evaluation performance of policy decision point (PDP) decreases obviously with the growing numbers of rules embodied in a policy. Aiming to improve the evaluation performance of PDP, a distributed policy evaluation engine called XDPEE is presented. In this engine, the unicity of PDP in the centralized authorization model is changed by increasing the number of PDPs. A policy should be decomposed into multiple subpolicies each with fewer rules by using a decomposition method, which can have the advantage of balancing the cost of subpolicies deployed to each PDP. Policy decomposition is the key problem of the evaluation performance improvement of PDPs. A greedy algorithm with time complexity for policy decomposition is constructed. In experiments, the policy of the LMS, VMS, and ASMS in real applications is decomposed separately into multiple subpolicies based on the greedy algorithm. Policy decomposition guarantees that the cost of subpolicies deployed to each PDP is equal or approximately equal. Experimental results show that (1) the method of policy decomposition improves the evaluation performance of PDPs effectively and that (2) the evaluation time of PDPs reduces with the growing numbers of PDPs.

Suggested Citation

  • Fan Deng & Ping Chen & Li-Yong Zhang & Xian-Qing Wang & Sun-De Li & Hui Xu, 2014. "Policy Decomposition for Evaluation Performance Improvement of PDP," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, May.
  • Handle: RePEc:hin:jnlmpe:610278
    DOI: 10.1155/2014/610278
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    Cited by:

    1. Fan Deng & Zhenhua Yu & Xinrui Zhan & Chongyu Wang & Xiaolin Zhang & Yangyang Zhang & Zilu Qin, 2022. "Poliseek: A Fast XACML Policy Evaluation Engine Using Dimensionality Reduction and Characterized Search," Mathematics, MDPI, vol. 10(23), pages 1-25, November.

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