A Novel Privacy Preserving Model for Datasets Re-Publication

Abstract:

Some medical records are often added and deleted in the practical applications. The leakage of privacy information caused by re-publishing datasets with multiple sensitive attributes becomes more likely than any other publication styles. In this paper, we first systematically characterize the inference attacks and set the hierarchy sensitive attribute rules. Then we propose a novel privacy preserving model based on k-anonymity for re-publication of multiple sensitive datasets and verify the novel approach that can eliminate inference channel and effectively protect privacy information in re-publication of datasets with multiple sensitive attributes by specific example. Finally, we present the anonymization algorithm to achieve privacy against inference attack.

• Periodical: Advanced Materials Research (Volumes 108-111)
• Pages: 1433-1438
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.108-111.1433
• Online since: May 2010
• Authors: Yan Zhao, Jian Wang, Qiu Sha Zhu, Jia Jin Le
• Keywords: Inference Channel, Multiple Sensitive Attributes, Privacy Preserving, Re-Publication

References

[1] L. Weeney: k-anonymity: a model for protecting privacy. International Journal on Uncertainty, Fuzziness and Knowledge-based Systems. (2002), pp.557-570.

DOI: 10.1142/s0218488502001648

[2] J. W. Byun, Y. Sohn, E. Bertion and N. Li: Secure anonymization for incremental datasets. In the SIAM Conference un Data Minin (SDM). (2006), pp.48-63.

[3] K. Wang, B. C. M. Fung: Anonymizing sequential releases. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. (2006), pp.414-423.

DOI: 10.1145/1150402.1150449

[4] B. C. M. Fung, K. Wang, et al: Anonymity for continuous data publishing. Proceedings of the 11th international conference on Extending database technology: Advances in database technology. (2008), pp.264-275.

DOI: 10.1145/1353343.1353378

[5] X. Xiao, Y. Tao: M-invariance: towards privacy preserving re-publication of dynamic datasets. In Proc. of SIGMOD, ACM Press, New York. (2007), pp.689-700.

DOI: 10.1145/1247480.1247556

[6] Y. Bu, A.W. Fu, et al: Privacy Preserving Serial Data Publishing by Role Composition. In VLDB. (2008), pp.845-856.

DOI: 10.14778/1453856.1453948

[7] Q. Wei, Y. Lu, L. Zou: ε-inclusion: privacy preserving re-publication of dynamic datasets. Journal of Zhejiang University SCIENCE A. Vol. 9 (2008), pp.1124-1133.

DOI: 10.1631/jzus.a071595

[8] A. Machanavajjhala, J. Gehrke, and D. Kifer, et al: ℓ-diversity: Privacy beyond k-anonymity. In Proc. of ICDE. (2006).

DOI: 10.1109/icde.2006.1

[9] X C. Yang, Y Z. Wang, etc: Privacy preserving approaches for multiple sensitive attributes in data publishing. Chinese Journal of Computers. Vol. 31(2008), pp.574-587.

DOI: 10.3724/sp.j.1016.2008.00574

[10] T S. Gal, Z Chen, A. Gangopadhyay: A Privacy Protection Model for patient data with multiple sensitive attributes. International Journal of Information Security and Privacy. Vol. 2(2008), p.2844.

DOI: 10.4018/jisp.2008070103

[11] Y. Ye, Y. Liu, C. Wang, etc: Decomposition: Privacy Preservation for Multiple Sensitive Attributes. DASFAA, LNCS 5463, (2009), pp.486-490.

[12] World Health Organization: International Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Second edition, Geneva, Vol. 2(2007).