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A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

Author

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  • Yanchao Zhang

    (Institute of ICT Security Research, China Academy of Information and Communications Technology, Beijing, China)

  • Qing Liu

    (Institute of ICT Security Research, China Academy of Information and Communications Technology, Beijing, China)

  • JunJun Cheng

    (China Information Technology Security Evaluation Center, Beijing, China)

  • JiJia Yang

    (Institute of ICT Security Research, China Academy of Information and Communications Technology, Beijing, China)

Abstract

Beyond l-diversity model, an algorithm (l-BDT) based on state decision tree is proposed in this paper, which aims at protecting multi-sensitive attributes from being attacked. The algorithm considers the whole situations in equivalence partitioning for the first, prunes the decision tree according to some conditions for the second, and screens out the method with the least information loss of equivalence partitioning for the last. The analysis and experiments show that the l-BDT algorithm has the best performance in controlling the information loss. It can be ensured that the published data is the most closed towards the original data, so as to ensure that the published data is as useful as possible.

Suggested Citation

  • Yanchao Zhang & Qing Liu & JunJun Cheng & JiJia Yang, 2016. "A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving," International Journal of Interdisciplinary Telecommunications and Networking (IJITN), IGI Global, vol. 8(2), pages 1-11, April.
    • Handle: RePEc:igg:jitn00:v:8:y:2016:i:2:p:1-11
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    Cited by:

    1. Ginger Zhe Jin, 2018. "Artificial Intelligence and Consumer Privacy," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 439-462, National Bureau of Economic Research, Inc.

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