Privacy Preservation of Semi-structured Data Based on XML

  • Cheng ShiEmail author
  • Mingda YangEmail author
  • Bo NingEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 517)


In the information age, people’s various behavioral data are collected in large quantities. The sharing of information makes it convenient for some scientific investigations, but there is a leakage of personal privacy at the same time. The current research on privacy preservation is mostly based on relational tables or social network graphs. This paper focuses on semi-structured data, which is often ignored in privacy preservation. We propose a new privacy guarantee called X-km-anonymity and propose a bottom-up heuristic algorithm that provides protection by satisfying X-km-anonymity. We verified the feasibility of the algorithm through a reliable utility analysis method on the simulation data.


Privacy preservation Semi-structured data X-km-anonymity XML 



This work is supported by the National Natural Science Foundation of China (U1401256), the National Natural Science Foundation of Liaoning province (201602094) and the National Natural Science Foundation of China under Grant Nos. 61602076.


  1. 1.
    Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 10(5), 557–570 (2002)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Gkountouna, O., Terrovitis, M.: Anonymizing collections of tree-structured data. IEEE Trans. Knowl. Data Eng. 27(8), 2034–2048 (2015)CrossRefGoogle Scholar
  3. 3.
    Landberg, A.H., Nguyen, K., Pardede, E., Rahayu, J.W.: δ-dependency for privacy-preserving XML data publishing. J. Biomed. Inf. 50, 77–94 (2014)CrossRefGoogle Scholar
  4. 4.
    Terrovitis, M., Mamoulis, N., Kalnis, P.: Privacy-preserving Anonymization of set-valued data. PVLDB 1(1), 115–125 (2008)Google Scholar
  5. 5.
    Al-Khalifa, S., Jagadish, H.V., Koudas, N., Patel, J., Srivastava, D., Wu, Y.: Structural joins: a primitive for efficient XML query pattern matching. In: Proceedings of the IEEE International on Data Engineering, 141–152 (2002)Google Scholar
  6. 6.
    Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: optimal XML pattern matching. ACM SIGMOD 310–321 (2002)Google Scholar
  7. 7.
    Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: K-anonymity and its enforcement through generalization and suppression. Technical report, CMU, SRI (1998)Google Scholar
  8. 8.
    Sweeney, L.: Achieving K-anonymity privacy protection using generalization and suppression. Int. J. Uncertain. Fuzziness Knowl.-based Syst. 10(5), 571–588 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zhao, N.N., Liang, Y.W.: Combining structure and content similarities measure for XML document. Microelectron. Comput. 33(4), 69–76 (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Dalian Maritime UniversityDalianChina
  2. 2.Cornell UniversityIthacaUSA

Personalised recommendations