A pairwise systematic microaggregation for statistical disclosure control

Kabir, Md Enamul and Wang, Hua and Zhang, Yanchun (2010) A pairwise systematic microaggregation for statistical disclosure control. In: 10th IEEE International Conference on Data Mining (ICDM 2010) , 14-17 Dec 2010, Sydney, Australia.


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. 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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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Sciences - Department of Maths and Computing (Up to 30 Jun 2013)
Date Deposited: 07 Apr 2011 04:34
Last Modified: 22 Feb 2015 23:59
Uncontrolled Keywords: privacy; microaggregation; microdata protection; disclosure control; k-anonymity
Fields of Research (2008): 01 Mathematical Sciences > 0104 Statistics > 010499 Statistics not elsewhere classified
08 Information and Computing Sciences > 0804 Data Format > 080402 Data Encryption
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: https://doi.org/10.1109/ICDM.2010.111
URI: http://eprints.usq.edu.au/id/eprint/18230

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