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Mutual Correlation-based Optimal Slicing for Preserving Privacy in Data Publishing

  • K. AshokaEmail author
  • B. Poornima
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)

Abstract

Privacy preservation is a substantial concern for the organizations that publish/share personal data for informal analysis. Several anonymization algorithms such as generalization and Bucketization are developed as a solution to this Privacy Preserving Data Publishing (PPDP). Latest research has shown that generalization loses significant amount of information, particularly for high dimensional data. However, Bucketization does not prevent membership disclosure. In this paper, we propose a novel approach that makes use of Information Gain of the attributes with respect to sensitive attributes, which gives the effectiveness of an attribute in classifying the data, which is two-way association among attributes. We show that our approach preserves better data utility and has lesser complexity than earlier techniques. Our proposed technique is theoretically analyzed, and mathematical analysis outstrips past works with sufficient experiments.

Keywords

Privacy preserving data Data anonymization Data perturbation Data utility 

References

  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.
    Samarati, P.: Protecting respondents’ identities in microdata release. IEEE Trans. Knowl. Data Eng. 13(6), 1010–1027 (2001). doi: https://doi.org/10.1109/69.971193CrossRefGoogle Scholar
  3. 3.
    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 ’05). ACM, New York, NY, USA, pp. 49–60 (2005)Google Scholar
  4. 4.
    LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Mondrian multidimensional k-anonymity. In: 22nd International Conference on Data Engineering (ICDE’06), pp. 25–25. doi: https://doi.org/10.1109/ICDE.2006.101 (2006)
  5. 5.
    Machanavajjhala, A., Gehrke, J., Kifer, D., Venkitasubramaniam, M.: ℓ-diversity: privacy beyond k-anonymity. In: Proceedings of International Conference Data Engineering (ICDE), p. 24 (2006)Google Scholar
  6. 6.
    Domingo-Ferrer, J., Torra, V.: A critique of k-anonymity and some of its enhancements. In: Proceedings of the 3rd International Conference on Availability, Reliability and Security (ARES), pp. 990–993 (2008)Google Scholar
  7. 7.
    Ninghui, L., Tiancheng, L., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and ℓ-diversity. In: Proceedings—International Conference on Data Engineering, pp. 106–115 (2007)Google Scholar
  8. 8.
    Xiao, X., Tao, Y.: m-invariance: towards privacy preserving re-publication of dynamic datasets. In: ACM SIGMOD International Conference on Management of Data, pp. 689–700 (2007)Google Scholar
  9. 9.
    Xiao, X., Tao, Y.: Personalized privacy preservation. In: Proceedings of ACM International Conference on Management of Data (SIGMOD), Chicago, IL (2006)Google Scholar
  10. 10.
    Li, T., Li, N., Zhang, J., Molloy, I.: Slicing: a new approach for privacy preserving data publishing. IEEE Trans. Knowl. Data Eng. 24(3), 561–574 (2012)CrossRefGoogle Scholar
  11. 11.
    Aggarwal, C.: On k-anonymity and the curse of dimensionality. In: Proceedings of International Conference on Very Large Data Bases (VLDB), pp. 901–909 (2005)Google Scholar
  12. 12.
    Kifer, D., Gehrke, J.: Injecting utility into anonymized data sets. In: Proceedings of ACM SIGMOD International Conference on Management of Data (SIGMOD), pp. 217–228 (2006)Google Scholar
  13. 13.
    Nergiz, M.E., Atzori, M., Clifton, C.: Hiding the presence of individuals from shared databases. In: Proceedings of ACM SIGMOD International Conference on Management of Data, pp. 665–676 (2007)Google Scholar
  14. 14.
    Kabir, M.E., Wang, H., Bertino, E.: Efficient systematic clustering method for k-anonymization. Acta Inf. 48(1), 51–66 (2011). doi: https://doi.org/10.1007/s00236-010-0131-6MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Pavan, R., Bhaladhare, A.N.D., Devesh, C.: Jinwala: novel approaches for privacy preserving data mining in k-anonymity model. J. Inf. Sci. Eng. 32(1), 63–78 (2016)Google Scholar
  16. 16.
    Tao, Y., Xiao, X., Li, J., Zhang, D.: On anti-corruption privacy-preserving publication. In: Proceedings of ICDE 08, Cancun, April 7–12, pp. 725–734. Washington, DC, USA (2008)Google Scholar
  17. 17.
    Zhu, H., Tian, S., Lü, K.: Privacy-preserving data publication with features of independent ℓ-diversity. Comput. J. 58(4), 549–571 (2015)CrossRefGoogle Scholar
  18. 18.
    Fengli, Z., Yijing, B.: ARM-based privacy preserving for medical data publishing. In: Cloud Computing and Security: First International Conference, ICCCS 2015, Nanjing, China, August 13–15. doi: https://doi.org/10.1007/978-3-319-27051-7_6 (2015)
  19. 19.
    Sánchez, D., Batet, M., Viejo, A.: Utility-preserving privacy protection of textual healthcare documents. J. Biomed. Inf. 52, 189–198 (2014). doi: https://doi.org/10.1016/j.jbi.2014.06.008CrossRefGoogle Scholar
  20. 20.
    Fan, L., Jin, H.: A practical framework for privacy-preserving data analytics. In: Proceedings of the 24th International Conference on World Wide Web (WWW ’15), pp. 311–321. ACM, New York (2015)Google Scholar
  21. 21.
    Zaman, N.K., Obimbo, C., Dara, R.A.: A novel differential privacy approach that enhances classification accuracy. In: Desai, E. (ed.) Proceedings of the Ninth International C* Conference on Computer Science and Software Engineering (C3S2E ’16), pp. 79–84. ACM, New York. doi:http://dx.doi.org/10.1145/2948992.2949027 (2016)
  22. 22.
    Weng, L., Amsaleg, L., Furon, T.: Privacy-preserving outsourced media search. IEEE Trans. Knowl. Data Eng. 28(10), 2738–2751 (2016). doi: https://doi.org/10.1109/TKDE.2016.2587258CrossRefGoogle Scholar
  23. 23.
    Lichman, M.: UCI Machine Learning Repository. (http://archive.ics.uci.edu/ml). Irvine, CA: University of California, School of Information and Computer Science (2013)

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Bapuji Institute of Engineering and TechnologyDavangereIndia

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