A Hybrid Method For K-Anonymization Using Clustering Technique

K-anonymity is a model to protect public released data from identification. These techniques have been focus of intense research in the last few years. An important requirement for such techniques is to minimize the information loss due to anonymization, it is crucial to group similar data together...

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Main Authors: Abd.Rahim, Y, Sahib, S., Abd Ghani, M. K., Yaacob, A.H
Format: Conference or Workshop Item
Language:English
Published: 2011
Subjects:
Online Access:http://eprints.utem.edu.my/id/eprint/363/
http://eprints.utem.edu.my/id/eprint/363/1/INCTS03-052017O.pdf
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author Abd.Rahim, Y
Sahib, S.
Abd Ghani, M. K.
Yaacob, A.H
author_facet Abd.Rahim, Y
Sahib, S.
Abd Ghani, M. K.
Yaacob, A.H
author_sort Abd.Rahim, Y
building UTeM Institutional Repository
collection Online Access
description K-anonymity is a model to protect public released data from identification. These techniques have been focus of intense research in the last few years. An important requirement for such techniques is to minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group individually. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality in k-anonymization model. This work compares the performance of two recently proposed clustering-based techniques for k-anonymization, and proposes a hybrid of both techniques to achieve less information loss than each of the original techniques. Based on results show that the proposed hybrid technique reduces not only the total information loss but also the variance of information loss among groups.
first_indexed 2025-11-15T19:46:25Z
format Conference or Workshop Item
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institution Universiti Teknikal Malaysia Melaka
institution_category Local University
language English
last_indexed 2025-11-15T19:46:25Z
publishDate 2011
recordtype eprints
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spelling utem-3632015-05-28T02:17:57Z http://eprints.utem.edu.my/id/eprint/363/ A Hybrid Method For K-Anonymization Using Clustering Technique Abd.Rahim, Y Sahib, S. Abd Ghani, M. K. Yaacob, A.H QA75 Electronic computers. Computer science K-anonymity is a model to protect public released data from identification. These techniques have been focus of intense research in the last few years. An important requirement for such techniques is to minimize the information loss due to anonymization, it is crucial to group similar data together and then anonymize each group individually. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality in k-anonymization model. This work compares the performance of two recently proposed clustering-based techniques for k-anonymization, and proposes a hybrid of both techniques to achieve less information loss than each of the original techniques. Based on results show that the proposed hybrid technique reduces not only the total information loss but also the variance of information loss among groups. 2011 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/363/1/INCTS03-052017O.pdf Abd.Rahim, Y and Sahib, S. and Abd Ghani, M. K. and Yaacob, A.H (2011) A Hybrid Method For K-Anonymization Using Clustering Technique. In: International Conference on Aerospace Engineering and Information Technology (AEIT 2011), May 5-6, 2011, Beijing China.
spellingShingle QA75 Electronic computers. Computer science
Abd.Rahim, Y
Sahib, S.
Abd Ghani, M. K.
Yaacob, A.H
A Hybrid Method For K-Anonymization Using Clustering Technique
title A Hybrid Method For K-Anonymization Using Clustering Technique
title_full A Hybrid Method For K-Anonymization Using Clustering Technique
title_fullStr A Hybrid Method For K-Anonymization Using Clustering Technique
title_full_unstemmed A Hybrid Method For K-Anonymization Using Clustering Technique
title_short A Hybrid Method For K-Anonymization Using Clustering Technique
title_sort hybrid method for k-anonymization using clustering technique
topic QA75 Electronic computers. Computer science
url http://eprints.utem.edu.my/id/eprint/363/
http://eprints.utem.edu.my/id/eprint/363/1/INCTS03-052017O.pdf