JSW 2012 Vol.7(11): 2415-2423 ISSN: 1796-217X
doi: 10.4304/jsw.7.11.2415-2423
doi: 10.4304/jsw.7.11.2415-2423
Microdata Protection Method Through Microaggregation: A Systematic approach
Md Enamul Kabir1 and Hua Wang2
1School of Engineering and Information Technology University of New South Wales at the Australian Defence Force Academy (UNSW@ADFA) Northcott Drive, Canberra ACT 2600
2Department of Mathematics and Computing University of Southern Queensland Toowoomba, QLD 4350, Australia
Abstract—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).
Index Terms—Privacy, Microaggregation, Microdata protection, k-anonymity, Disclosure control
2Department of Mathematics and Computing University of Southern Queensland Toowoomba, QLD 4350, Australia
Abstract—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).
Index Terms—Privacy, Microaggregation, Microdata protection, k-anonymity, Disclosure control
Cite: Md Enamul Kabir and Hua Wang, "Microdata Protection Method Through Microaggregation: A Systematic approach," Journal of Software vol. 7, no. 11, pp. 2415-2423, 2012.
General Information
ISSN: 1796-217X (Online)
Frequency: Quarterly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, CNKI, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsweditorialoffice@gmail.com
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