Microdata protection method through microaggregation: A systematic approach

被引：2

作者：

Kabir, Md Enamul ^{[1
]}

Wang, Hua ^{[2
]}

机构：

[1] School of Engineering and Information Technology, University of New South Wales at the Australian Defence Force Academy, Northcott Drive

[2] Department of Mathematics and Computing, University of Southern Queensland, Toowoomba

来源：

Journal of Software | 2012年 / 7卷 / 11期

关键词：

Disclosure control; K-anonymity; Microaggregation; Microdata protection; Privacy;

D O I：

10.4304/jsw.7.11.2415-2423

中图分类号：

学科分类号：

摘要：

Microdata protection in statistical databases has recently become a major societal concern and has been intensively studied in recent years. Statistical Disclosure Control (SDC) is often applied to statistical databases before they are released for public use. Microaggregation for SDC is a family of methods to protect microdata from individual identification. SDC seeks to protect microdata in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Microaggregation works by partitioning the microdata into groups of at least k records and then replacing the records in each group with the centroid of the group. This paper presents a clustering-based microaggregation method to minimize the information loss. The proposed technique adopts to group similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of systematic clustering problem is defined and investigated and an algorithm of the proposed problem is developed. Experimental results show that our method attains a reasonable dominance with respect to both information loss and execution time than the most popular heuristic algorithm called Maximum Distance to Average Vector (MDAV). © 2012 ACADEMY PUBLISHER.

引用

页码：2415 / 2423

页数：8

共 30 条

[11]

Han J.-M., Cen T.-T., Yu H.-Q., Yu J., A multivariate immune clonal selection microaggregation algorithm, Proc. IEEE International Conference On Granular Computing, pp. 252-256, (2008)

[12]

Hansen S., Mukherjee S., A polynomial algorithm for optimal univariate microaggregation, IEEE Transactions on Knowledge and Data Engineering, 15, 4, pp. 1043-1044, (2003)

[13]

Kabir M.E., Wang H., Systematic Clustering-based Microaggregation for Statitical Disclosure Control, Proc. 4th International Conference On Network and System Security, pp. 435-441, (2010)

[14]

Kabir M.E., Wang H., Zhang Y., A Pairwise-Systematic Microaggregation for Statitical Disclosure Control, Proc. 10th IEEE International Conference on Data Mining, pp. 266-273, (2010)

[15]

Kabir M.E., Wang H., Bertino E., Efficient systematic clustering method for k-anonymization, Acta Informatica, 48, 1, pp. 51-66, (2011)

[16]

Kabir M.E., Wang H., Microdata Protection Method Through Microaggragation: A Median Based Approach, Information Security Journal: A Global Perspective, 20, 1, pp. 1-8, (2011)

[17]

Laszlo M., Mukherjee S., Minimum spanning tree partitioning algorithm for microaggregation, IEEE Transactions on Knowledge and Data Engineering, 17, 7, pp. 902-911, (2005)

[18]

Martinez-Balleste A., Solanas A., Domingo-Ferrer J., Mateo-Sanz J.M., A genetic approach to multivariate microaggregation for database privacy, Proc. IEEE 23rd International Conference on Data Engineering Workshop, pp. 180-185, (2007)

[19]

Oganian A., Domingo-Ferrer J., On the complexity of optimal microaggregation for statistical disclosure control, Statistical Journal of the United Nations Economic Commission For Europe, 18, pp. 345-354, (2001)

[20]

Solanas A., Martinez-Balleste A., Mateo-Sanz J., Domingo-Ferrer J., Multivariate microaggregation based genetic algorithms, Proc. IEEE Third International Conference on Intelligent Systems, pp. 65-70, (2006)

← 1 2 3 →