A Privacy-Aware Access Model on Anonymized Data

  • Xuezhen HuangEmail author
  • Jiqiang Liu
  • Zhen Han
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9473)


With development of information technology and communication, corporations and individuals will collect some digital information to support information-based decisions. However, under some conditions, if all original data are released, some privacy will be disclosed, which will threaten data security and data privacy. Therefore, data owners will take some security measures. Role-based access control may authorize related original data accessed by users according to their roles. Privacy-preserving technology release processed data to avoid privacy disclosure. Nevertheless, existing privacy-preserving technologies lack continuity and are quite inefficient. This paper establishes an access model about on anonymized data and combines with the foregoing two security measures. On the premise that data security and data privacy are ensured, there is more flexibility and diversity and work efficiency is improved as well.


Privacy Data security Access control Anonymity 


  1. 1.
    Abdalaal, A., Nergiz, M.E., Saygin, Y.: Privacy-preserving publishing of opinion polls. Comput. Security 37, 143–154 (2013)CrossRefGoogle Scholar
  2. 2.
    Bu, Y., Fu, A.W.C., Wong, R.C.W., Chen, L., Li, J.: Privacy preserving serial data publishing by role composition. Proc. VLDB Endowment 1(1), 845–856 (2008)CrossRefGoogle Scholar
  3. 3.
    David, F., Richard, K.: Role-based access controls. In: Proceedings of 15th NIST-NCSC National Computer Security Conference, vol. 563. NIST-NCSC, Baltimore, Maryland (1992)Google Scholar
  4. 4.
    Fung, B., Wang, K., Fu, A.W.C., Pei, J.: Anonymity for continuous data publishing. In: Proceedings of the 11th International Conference on Extending Database Technology: Advances in Database Technology, pp. 264–275. ACM (2008)Google Scholar
  5. 5.
    Huang, X., Liu, J., Han, Z., Yang, J.: A new anonymity model for privacy-preserving data publishing. China Commun. 11(9), 47–59 (2014)CrossRefGoogle Scholar
  6. 6.
    LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: efficient full-domain k-anonymity. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data. SIGMOD 2005, pp. 49–60. ACM, New York, NY, USA (2005)Google Scholar
  7. 7.
    Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007, pp. 106–115 (2007)Google Scholar
  8. 8.
    Li, N., Li, T., Venkatasubramanian, S.: Closeness: a new privacy measure for data publishing. IEEE Trans. Knowl. Data Eng. 22(7), 943–956 (2010)CrossRefGoogle Scholar
  9. 9.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: \(l\)-diversity: privacy beyond k-anonymity. In: 2013 IEEE 29th International Conference on Data Engineering (ICDE) 0, 24 (2006)Google Scholar
  10. 10.
    Ni, Q., Bertino, E., Lobo, J., Brodie, C., Karat, C.M., Karat, J., Trombeta, A.: Privacy-aware role-based access control. ACM Trans. Inf. Syst. Security (TISSEC) 13(3), 24 (2010)Google Scholar
  11. 11.
    Shmueli, E., Tassa, T., Wasserstein, R., Shapira, B., Rokach, L.: Limiting disclosure of sensitive data in sequential releases of databases. Inf. Sci. 191, 98–127 (2012)CrossRefGoogle Scholar
  12. 12.
    Sun, X., Sun, L., Wang, H.: Extended k-anonymity models against sensitive attribute disclosure. Comput. Commun. 34(4), 526–535 (2011). Special issue: Building Secure Parallel and Distributed Networks and SystemsCrossRefGoogle Scholar
  13. 13.
    Sweeney, L.: \(k\)-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)zbMATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Wong, R.C.W., Li, J., Fu, A.W.C., Wang, K.: (\(\alpha \), \(k\))-anonymity: an enhanced k-anonymity model for privacy preserving data publishing. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD 2006, pp. 754–759. ACM, New York, NY, USA (2006)Google Scholar
  15. 15.
    Xiao, X., Tao, Y.: Personalized privacy preservation. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data. SIGMOD 2006, pp. 229–240 (2006)Google Scholar
  16. 16.
    Xiao, X., Tao, Y.: M-invariance: towards privacy preserving re-publication of dynamic datasets. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. SIGMOD 2007, pp. 689–700. ACM, New York, NY, USA (2007)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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