Skip to main content

Advertisement

Springer Nature Link
Log in

An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

At present, most of privacy preserving approaches in data publishing are applied to single sensitive attribute. However, applying single-sensitive-attribute privacy preserving techniques directly into data with multiple sensitive attributes often causes leakage of large amount of private information. This paper focuses on the privacy preserving methods in data publishing for multiple sensitive attributes. It combines data anonymous methods based on lossy join with the idea of clustering. And it proposes an improved algorithm of individuation K-anonymity for multiple sensitive attributes—\( MSA(\alpha ,l) \) algorithm. By setting parameters \( \alpha \) and \( l \), it can restrain sensitive attribute values in equivalence class, to make a more balanced distribution of sensitive attributes and satisfy the demand of diversity, then this algorithm is applied to K-anonymity model. Finally, the result of experiment shows that this improved model can preserve the privacy of sensitive data, and it can also reduce the information hidden rate.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Zhou, S. G., Li, F., Tao, Y. F., & Xiao, X. K. (2009). Privacy preservation in database applications: A survey. Chinese Journal of Computers, 32(5), 847–861.

    Article  Google Scholar 

  2. Liu, X. Y., Wang, B., & Yang, X. C. (2014). Survey on privacy preserving techniques for publishing social network data. Journal of Software, 25(3), 576–590.

    Google Scholar 

  3. Mehmood, A., Natgunanathan, I., Xiang, Y., et al. (2016). Protection of big data privacy. IEEE Access, 4, 1821–1834.

    Article  Google Scholar 

  4. Yi, X., Bouguettaya, A., Georgakopoulos, D. et al. (2015). Privacy protection for wireless medical sensor data. IEEE Transactions on Dependable & Secure Computing, 13(3), 1–1.

  5. Sweeney, L. (2002). K-anonymity: A model for protecting privacy. International Journal on Uncertainty, Fuzziness, and Knowledge Based Systems, 10(5), 557–570.

    Article  MathSciNet  MATH  Google Scholar 

  6. Casino, F., Domingo-Ferrer, J., Patsakis, C., et al. (2015). A k-anonymous approach to privacy preserving collaborative filtering. Journal of Computer and System Sciences, 81(6), 1000–1011.

    Article  Google Scholar 

  7. Maheshwarkar, B., Patidar, P., Rawa, M. K. et al. (2016). K-AMOA: K-Anonymity model for multiple overlapped attributes. In International conference on information and communication technology for competitive strategies. ACM.

  8. Dai, I., Kikuchi, R., Chida, K. et al. (2015). k-Anonymous microdata release via post randomisation method. In Advances in Information and Computer Security. Springer International Publishing (pp. 225–241).

  9. Han, J. M., Yu, J., Yu, H. Q., & Jia, J. (2011). A multi-level-diversity model for numerical sensitive attributes. Journal of Computer Research and Development, 48(1), 147–158.

    Google Scholar 

  10. Josep, Domingo-Ferrer, & Jordi, Soria-Comas. (2015). From t-closeness to differential privacy and vice versa in data anonymization. Knowledge-Based Systems, 74(1), 151–158.

    Google Scholar 

  11. Han, J. M., Yu, J., Yu, H. J., & Jia, J. (2010). Individuation privacy preservation oriented to sensitive values. Acta Electronica Sinica, 38(7), 1723–1728.

    Google Scholar 

  12. Ren, X., Jia B., & Wang K. (2013). Anatomy: Uncertain data k-anonymity privacy protection algorithm. In 2nd International conference on mechatronics and control engineering (ICMCE 2013). Dalian, Peoples Republic China.

  13. Zheng, Yuhui, Jeon, Byeungwoo, Danhua, Xu, Jonathan Wu, Q. M., & Zhang, H. (2015). Image segmentation by generalized hierarchical fuzzy C-means algorithm. Journal of Intelligent and Fuzzy Systems, 28(2), 961–973.

    Google Scholar 

  14. Yang, X. C., Wang, Y. Z., Wang, B., & Yu, G. (2008). Privacy preserving approaches for multiple sensitive attributes in data publishing. Chinese Journal of Computers, 31(4), 574–587.

    Article  Google Scholar 

  15. Xie, J., Zhang, J. P., Yang, J., & Zhang, B. A. (2014). Privacy preserving approach based on attributes correlation partition for multiple sensitive attributes. Acta Electronica Sinica, 42(9), 1718–1723.

    Google Scholar 

  16. Mubark, A. A., Elabd, E., & Abdulkader, H. (2016). Semantic anonymization in publishing categorical sensitive attributes. In International conference on knowledge and smart technology. IEEE.

  17. Yang, G., Li, J., Zhang, S. et al. (2013). An enhanced l-diversity privacy preservation. In International conference on fuzzy systems and knowledge discovery (pp. 1115–1120).

  18. Xiao, X., & Tao, Y. (2006). Anatomy: Simple and effective privacy pres-Ervation. In Proceedings of the 32nd international conference on very large data bases. Seoul, Korea (pp. 139–150).

  19. Machanavajjhala, A., Gehrke, J., & Kifer, D. (2007). l-diversity :Privacy beyond k-anonymity. ACM Transactions on Knowledge Discovery from Data, 1(1), 1–52.

    Article  Google Scholar 

  20. Sun, J. G., Liu, J., & Zhao, L. Y. (2008). Clustering algorithms research. Journal of Software, 19(1), 48–61.

    Article  MATH  Google Scholar 

  21. Chong, Z. H., Ni, W. W., Liu, T. T., & Zhang, Y. (2010). A privacy-preserving data publishing algorithm for clustering application. Journal of Computer Research and Development, 47(12), 2083–2089.

    Google Scholar 

  22. Sun, K., Liu, J., & Wang, X. Y. (2009). K-means cluster algorithm with refined initial center. Journal of Shenyang Normal University (Natural Science), 27(4), 448–450.

    Google Scholar 

  23. Qi, J., Yu, Y., Wang, L. et al. (2016). K*-means: An effective and efficient k-means clustering algorithm. In IEEE international conferences on big data and cloud computing (BDCloud), social computing and networking (SocialCom), sustainable computing and communications (SustainCom) (BDCloud-SocialCom-SustainCom). IEEE Computer Society (pp. 242–249).

  24. Ye, H., Lv, H., & Sun, Q. (2016). An improved semi-supervised K-means clustering algorithm. In IEEE information technology, networking, electronic and automation control conference.

  25. Li, J. T., Liu, Y. H., & Hao, Y. (2016). The improvement and application of a K-means clustering algorithm. In IEEE international conference on cloud computing and big data analysis. IEEE.

Download references

Acknowledgements

This paper was supported by the National Natural Science Foundation of China (61402241, 61572260, 61373017, 61572261, 61472192); Scientific and Technological Support Project of Jiangsu Province (BE2015702).

Author information

Authors and Affiliations

  1. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China

    Lin Zhang, Jie Xuan, Ruoqian Si & Ruchuan Wang

  2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, 210003, Jiangsu Province, China

    Lin Zhang & Ruchuan Wang

Authors
  1. Lin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jie Xuan
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ruoqian Si
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ruchuan Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Lin Zhang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Xuan, J., Si, R. et al. An Improved Algorithm of Individuation K-Anonymity for Multiple Sensitive Attributes. Wireless Pers Commun 95, 2003–2020 (2017). https://doi.org/10.1007/s11277-016-3922-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-016-3922-4

Keywords