A Pairwise-Systematic Microaggregation for Statistical Disclosure Control

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Kabir, M, Wang, Hua and Zhang, Yanchun (2010) A Pairwise-Systematic Microaggregation for Statistical Disclosure Control. In: 2010 IEEE International Conference on Data Mining. Webb, Geoffrey I, Liu, Bing, Zhang, Chengqi, Gunopulos, Dimitrios and Wu, Xindong, eds. IEEE, Los Alamitos, California, pp. 266-273.


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. Micro aggregation for SDC is a family of methods to protect micro data from individual identification. SDC seeks to protect micro data in such a way that can be published and mined without providing any private information that can be linked to specific individuals. Micro aggregation works by partitioning the micro data into groups of at least k records and then replacing the records in each group with the centroid of the group. An optimal micro aggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the micro aggregation process. This paper presents a pair wise systematic (P-S) micro aggregation method to minimize the information loss. The proposed technique simultaneously forms two distant groups at a time with the corresponding similar records together in a systematic way and then anonymized with the centroid of each group individually. The structure of P-S problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the P-S algorithm is compared against the most recent micro aggregation methods. Experimental results show that P-S algorithm incurs less than half information loss than the latest micro aggregation methods for all of the test situations.

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Proceedings of a meeting held 13-17 December 2010, Sydney, Australia

Item type Book Section
URI https://vuir.vu.edu.au/id/eprint/9969
DOI 10.1109/ICDM.2010.111
Official URL http://ieeexplore.ieee.org/xpl/articleDetails.jsp?...
ISBN 9781424491315 (print) 9780769542560 (online)
Subjects Historical > FOR Classification > 0804 Data Format
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords ResPubID21646, data mining, data privacy, security of data, set theory, statistical databases, microdata protection, microaggregation, k-anonymity, disclosure control
Citations in Scopus 5 - View on Scopus
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