Mahmood, Abdun Naser and Kabir, Md Enamul 
ORCID: https://orcid.org/0000-0002-6157-2753 and Mustofa, Abdul K.
(2013)
New multi-dimensional sorting based k-anonymity microaggregation for statistical disclosure control.
In: 8th International ICST Conference, SecureComm 2012: Security and Privacy in Communication Networks, 3-5 Sept 2012, Padua, Italy.
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Text (Accepted Version)
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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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Statistics for this ePrint Item |
| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
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| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Accepted Version deposited in accordance with the copyright policy of the publisher. |
| 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: | 16 Mar 2016 00:01 |
| Last Modified: | 13 Feb 2017 04:43 |
| Uncontrolled Keywords: | privacy; microaggregation; microdata protection; k-anonymity |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified |
| 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/10.1007/978-3-642-36883-7_16 |
| URI: | http://eprints.usq.edu.au/id/eprint/26880 |
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