Publication:
Shifting Dataset to Preserve Data Privacy

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dc.citedby2
dc.contributor.authorPozi M.S.M.en_US
dc.contributor.authorBakar A.A.en_US
dc.contributor.authorIsmail R.en_US
dc.contributor.authorYussof S.en_US
dc.contributor.authorRahim F.A.en_US
dc.contributor.authorRamli R.en_US
dc.contributor.authorid57219746822en_US
dc.contributor.authorid35178991300en_US
dc.contributor.authorid15839357700en_US
dc.contributor.authorid16023225600en_US
dc.contributor.authorid57350579500en_US
dc.contributor.authorid57191413657en_US
dc.date.accessioned2023-05-29T07:27:20Z
dc.date.available2023-05-29T07:27:20Z
dc.date.issued2019
dc.descriptionClassification (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 privacyen_US
dc.description.abstractData 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.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8632641
dc.identifier.doi10.1109/IC3e.2018.8632641
dc.identifier.epage139
dc.identifier.scopus2-s2.0-85062865666
dc.identifier.spage134
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85062865666&doi=10.1109%2fIC3e.2018.8632641&partnerID=40&md5=897b2e7589c1cd52655fd36105fe3a96
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24806
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2018 IEEE Conference on e-Learning, e-Management and e-Services, IC3e 2018
dc.titleShifting Dataset to Preserve Data Privacyen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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