Privacy preserving data mining using anonymization and K-means clustering on labor dataset

Privacy Preserving Data Mining (PPDM) has recently become an important research area. There are some issues and problems related to PPDM have been identified. Information loss occur when the original of data are modified to keep the privacy of those data. Effects of PPDM also cause the level of data...

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Main Author:	Ahmad Zahari, Samahah Solehah
Format:	Thesis
Language:	English
Published:	2019
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/96295/1/SamahahSolehahMSC2019.pdf.pdf
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id	my-utm-ep.96295
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spelling	my-utm-ep.962952022-07-12T08:16:13Z Privacy preserving data mining using anonymization and K-means clustering on labor dataset 2019 Ahmad Zahari, Samahah Solehah QA75 Electronic computers. Computer science Privacy Preserving Data Mining (PPDM) has recently become an important research area. There are some issues and problems related to PPDM have been identified. Information loss occur when the original of data are modified to keep the privacy of those data. Effects of PPDM also cause the level of data quality become lower. Aim of this research is to minimize information loss and increase the accuracy of mining result while maintaining the privacy level of data. A randomization approach based on anonymization and clustering algorithms are proposed in order to minimize the information loss and improve the accuracy of data clustering quality for PPDM results. Anonymization method is used in order to generalize and supress the data and limit the disclosure risk. Besides, the accuracy of data mining results could be increased by applying clustering using K-Means and EM algorithms. Labor dataset is used in this research and all instances are numerical value. WEKA tool is used to perform clustering algorithm on the labor dataset. Outcome for this research is the privacy level of dataset was increased while the information loss is minimized. The experimental results also show that the proposed method provides better result in privacy level of data mining. 2019 Thesis http://eprints.utm.my/id/eprint/96295/ http://eprints.utm.my/id/eprint/96295/1/SamahahSolehahMSC2019.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:143456 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Ahmad Zahari, Samahah Solehah Privacy preserving data mining using anonymization and K-means clustering on labor dataset
description	Privacy Preserving Data Mining (PPDM) has recently become an important research area. There are some issues and problems related to PPDM have been identified. Information loss occur when the original of data are modified to keep the privacy of those data. Effects of PPDM also cause the level of data quality become lower. Aim of this research is to minimize information loss and increase the accuracy of mining result while maintaining the privacy level of data. A randomization approach based on anonymization and clustering algorithms are proposed in order to minimize the information loss and improve the accuracy of data clustering quality for PPDM results. Anonymization method is used in order to generalize and supress the data and limit the disclosure risk. Besides, the accuracy of data mining results could be increased by applying clustering using K-Means and EM algorithms. Labor dataset is used in this research and all instances are numerical value. WEKA tool is used to perform clustering algorithm on the labor dataset. Outcome for this research is the privacy level of dataset was increased while the information loss is minimized. The experimental results also show that the proposed method provides better result in privacy level of data mining.
format	Thesis
qualification_level	Master's degree
author	Ahmad Zahari, Samahah Solehah
author_facet	Ahmad Zahari, Samahah Solehah
author_sort	Ahmad Zahari, Samahah Solehah
title	Privacy preserving data mining using anonymization and K-means clustering on labor dataset
title_short	Privacy preserving data mining using anonymization and K-means clustering on labor dataset
title_full	Privacy preserving data mining using anonymization and K-means clustering on labor dataset
title_fullStr	Privacy preserving data mining using anonymization and K-means clustering on labor dataset
title_full_unstemmed	Privacy preserving data mining using anonymization and K-means clustering on labor dataset
title_sort	privacy preserving data mining using anonymization and k-means clustering on labor dataset
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Computing
publishDate	2019
url	http://eprints.utm.my/id/eprint/96295/1/SamahahSolehahMSC2019.pdf.pdf
_version_	1747818656331464704

Privacy preserving data mining using anonymization and K-means clustering on labor dataset

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