Privacy preserving association rule mining using attribute-identity mapping

Association rule mining uncovers hidden yet important patterns in data. Discovery of the patterns helps data owners to make right decision to enhance efficiency, increase profit and reduce loss. However, there is privacy concern especially when the data owner is not the miner or when many parties ar...

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Main Author:	Jafar, Ibraheem
Format:	Thesis
Language:	English
Published:	2017
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/85859/1/IbraheemJafarMFC2017.pdf
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spelling	my-utm-ep.858592020-07-30T07:35:19Z Privacy preserving association rule mining using attribute-identity mapping 2017 Jafar, Ibraheem QA75 Electronic computers. Computer science Association rule mining uncovers hidden yet important patterns in data. Discovery of the patterns helps data owners to make right decision to enhance efficiency, increase profit and reduce loss. However, there is privacy concern especially when the data owner is not the miner or when many parties are involved. This research studied privacy preserving association rule mining (PPARM) of horizontally partitioned and outsourced data. Existing research works in the area concentrated mainly on the privacy issue and paid very little attention to data quality issue. Meanwhile, the more the data quality, the more accurate and reliable will the association rules be. Consequently, this research proposed Attribute-Identity Mapping (AIM) as a PPARM technique to address the data quality issue. Given a dataset, AIM identifies set of attributes, attribute values for each attribute. It then assigns ‘unique’ identity for each of the attributes and their corresponding values. It then generates sanitized dataset by replacing each attribute and its values with their corresponding identities. For privacy preservation purpose, the sanitization process will be carried out by data owners. They then send the sanitized data, which is made up of only identities, to data miner. When any or all the data owners need(s) ARM result from the aggregate data, they send query to the data miner. The query constitutes attributes (in form of identities), minSup and minConf thresholds and then number of rules they are want. Results obtained show that the PPARM technique maintains 100% data quality without compromising privacy, using Census Income dataset. 2017 Thesis http://eprints.utm.my/id/eprint/85859/ http://eprints.utm.my/id/eprint/85859/1/IbraheemJafarMFC2017.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:132391 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Jafar, Ibraheem Privacy preserving association rule mining using attribute-identity mapping
description	Association rule mining uncovers hidden yet important patterns in data. Discovery of the patterns helps data owners to make right decision to enhance efficiency, increase profit and reduce loss. However, there is privacy concern especially when the data owner is not the miner or when many parties are involved. This research studied privacy preserving association rule mining (PPARM) of horizontally partitioned and outsourced data. Existing research works in the area concentrated mainly on the privacy issue and paid very little attention to data quality issue. Meanwhile, the more the data quality, the more accurate and reliable will the association rules be. Consequently, this research proposed Attribute-Identity Mapping (AIM) as a PPARM technique to address the data quality issue. Given a dataset, AIM identifies set of attributes, attribute values for each attribute. It then assigns ‘unique’ identity for each of the attributes and their corresponding values. It then generates sanitized dataset by replacing each attribute and its values with their corresponding identities. For privacy preservation purpose, the sanitization process will be carried out by data owners. They then send the sanitized data, which is made up of only identities, to data miner. When any or all the data owners need(s) ARM result from the aggregate data, they send query to the data miner. The query constitutes attributes (in form of identities), minSup and minConf thresholds and then number of rules they are want. Results obtained show that the PPARM technique maintains 100% data quality without compromising privacy, using Census Income dataset.
format	Thesis
qualification_level	Master's degree
author	Jafar, Ibraheem
author_facet	Jafar, Ibraheem
author_sort	Jafar, Ibraheem
title	Privacy preserving association rule mining using attribute-identity mapping
title_short	Privacy preserving association rule mining using attribute-identity mapping
title_full	Privacy preserving association rule mining using attribute-identity mapping
title_fullStr	Privacy preserving association rule mining using attribute-identity mapping
title_full_unstemmed	Privacy preserving association rule mining using attribute-identity mapping
title_sort	privacy preserving association rule mining using attribute-identity mapping
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2017
url	http://eprints.utm.my/id/eprint/85859/1/IbraheemJafarMFC2017.pdf
_version_	1747818465743339520

Privacy preserving association rule mining using attribute-identity mapping

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