K−means clustering microaggregation for statistical disclosure control

Kabir, Md Enamul and Mahmood, Abdur Naser and Mustafa, Abdul K. (2012) K−means clustering microaggregation for statistical disclosure control. In: 2012 International Conference on Advances in Computing (ICADC 2012), 4-6 July 2012, Bangalore.

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Abstract

This paper presents a K-means clustering technique that satisfies the bi-objective function to minimize the information loss and maintain k-anonymity. The
proposed technique starts with one cluster and subsequently partitions the dataset into two or more clusters such that the total information loss across all clusters is the least, while satisfying the k-anonymity requirement. The structure of K− means clustering problem is defined and investigated and an algorithm of the proposed problem is developed. The performance of the K− means clustering algorithm is compared against the most recent microaggregation methods. Experimental results show that K− means clustering algorithm incurs less information loss than the latest microaggregation 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: Accepted version deposited in accordance with the copyright policy of the publisher.
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 15 Mar 2016 05:30
Last Modified: 30 Jun 2017 03:05
Uncontrolled Keywords: K−means clustering, microaggregation, tatistical disclosure control
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-81-322-0740-5_135
URI: http://eprints.usq.edu.au/id/eprint/26884

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