Shifting Dataset to Preserve Data Privacy

Classification (of information); Data mining; E-learning; Large dataset; Learning systems; Classification tasks; Data attributes; Dataset shifts; Generative model; Kernel Density Estimation; Privacy preservation; Privacy preserving; Synthetic data; Data privacy

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Bibliographic Details
Main Authors:	Pozi M.S.M., Bakar A.A., Ismail R., Yussof S., Rahim F.A., Ramli R.
Other Authors:	57219746822
Format:	Conference Paper
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
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Institution:	Universiti Tenaga Nasional

id	my.uniten.dspace-24806
record_format	dspace
spelling	my.uniten.dspace-248062023-05-29T15:27:20Z Shifting Dataset to Preserve Data Privacy Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R. 57219746822 35178991300 15839357700 16023225600 57350579500 57191413657 Classification (of information); Data mining; E-learning; Large dataset; Learning systems; Classification tasks; Data attributes; Dataset shifts; Generative model; Kernel Density Estimation; Privacy preservation; Privacy preserving; Synthetic data; Data privacy Data analytic is very valuable in any domain that produces large amount of data making demands on full datasets to be revealed for analytic purposes are rising. Regardless, the privacy of the released dataset should be preserved. New techniques using synthetic data as a mean to preserve the privacy has been identified as appropriate approach to fulfill the demand. In this paper, a privacy-preserving data synthetic framework for data analytic is proposed. Using a generative model that captures the density function of data attributes, the privacy-preserving synthetic data is produced. We performed classification task through various machine learning classifiers in measuring the data utility of the new privacy-preserving synthesized data. � 2018 IEEE. Final 2023-05-29T07:27:20Z 2023-05-29T07:27:20Z 2019 Conference Paper 10.1109/IC3e.2018.8632641 2-s2.0-85062865666 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062865666&doi=10.1109%2fIC3e.2018.8632641&partnerID=40&md5=897b2e7589c1cd52655fd36105fe3a96 https://irepository.uniten.edu.my/handle/123456789/24806 8632641 134 139 Institute of Electrical and Electronics Engineers Inc. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Classification (of information); Data mining; E-learning; Large dataset; Learning systems; Classification tasks; Data attributes; Dataset shifts; Generative model; Kernel Density Estimation; Privacy preservation; Privacy preserving; Synthetic data; Data privacy
author2	57219746822
author_facet	57219746822 Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R.
format	Conference Paper
author	Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R.
spellingShingle	Pozi M.S.M. Bakar A.A. Ismail R. Yussof S. Rahim F.A. Ramli R. Shifting Dataset to Preserve Data Privacy
author_sort	Pozi M.S.M.
title	Shifting Dataset to Preserve Data Privacy
title_short	Shifting Dataset to Preserve Data Privacy
title_full	Shifting Dataset to Preserve Data Privacy
title_fullStr	Shifting Dataset to Preserve Data Privacy
title_full_unstemmed	Shifting Dataset to Preserve Data Privacy
title_sort	shifting dataset to preserve data privacy
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2023
_version_	1806426249195683840

Shifting Dataset to Preserve Data Privacy

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