Novel iterative min-max clustering to minimize information loss in statistical disclosure control

Mahmood, Abdun Naser and Kabir, Md Enamul and Mustafa, Abdul K. (2014) Novel iterative min-max clustering to minimize information loss in statistical disclosure control. In: 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014), 24-26 Sept 2014, Beijing, China.

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Abstract

In recent years, there has been an alarming increase of online identity theft and attacks using personally identifiable information. The goal of privacy preservation is to de-associate individuals from sensitive or microdata information. Microaggregation techniques 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 microaggregation method must minimize the information loss resulting from this replacement process. The challenge is how to minimize the information loss during the microaggregation process. This paper presents a new microaggregation technique for
Statistical Disclosure Control (SDC). It consists of two stages. In the first stage, the algorithm sorts all the records in the data set in a particular way to ensure that during microaggregation very dissimilar observations are never entered into the same cluster. In the second stage an optimal microaggregation method is used to create k-anonymous clusters while minimizing the information loss. It works by taking the sorted data and simultaneously creating two distant clusters using the two extreme sorted values as seeds for the clusters. The performance of the proposed technique is compared against the most recent microaggregation methods. Experimental results using benchmark datasets show that the proposed algorithm has the lowest information loss compared with a basket of
techniques in the literature.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: No evidence of copyright restrictions preventing deposit.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 09 Jul 2015 06:14
Last Modified: 23 Feb 2017 05:54
Uncontrolled Keywords: privacy; microaggregation; microdata protection; k-anonymity disclosure control
Fields of Research : 08 Information and Computing Sciences > 0899 Other Information and Computing Sciences > 089999 Information and Computing Sciences not elsewhere classified
08 Information and Computing Sciences > 0803 Computer Software > 080303 Computer System Security
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-3-319-23802-9_14
URI: http://eprints.usq.edu.au/id/eprint/27517

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