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dc.contributor.authorBinJubier, Mohammed
dc.contributor.authorAhmed, Abdulghani Ali
dc.contributor.authorIsmail, Mohd Arfian Bin
dc.contributor.authorSadiq, Ali Safaa
dc.contributor.authorKhan, Muhammad Khurram
dc.date.accessioned2020-01-16T10:14:54Z
dc.date.available2020-01-16T10:14:54Z
dc.date.issued2019-12-25
dc.identifier.citationBin Jubier, M., Ahmed, A. A., Bin Ismail, M. A., Sadiq, A. S. and Khan, M. K. (2020) Comprehensive survey on big data privacy protection, IEEE Access, 8, pp. 20067-20079. DOI: 10.1109/ACCESS.2019.2962368.en
dc.identifier.issn2169-3536en
dc.identifier.doi10.1109/access.2019.2962368en
dc.identifier.urihttp://hdl.handle.net/2436/622988
dc.description.abstractIn recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of confidential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a specific approach to enable the development of a good data mining model on modified data, thereby meeting a specified privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals’ sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classified using various approaches for data modification. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectsecurityen
dc.subjectbig dataen
dc.subjectprivacy protectionen
dc.subjectprivacy-preserving data miningen
dc.titleComprehensive survey on big data privacy protectionen
dc.typeJournal articleen
dc.identifier.eissn2169-3536
dc.identifier.journalIEEE Accessen
dc.date.updated2020-01-14T16:15:02Z
dc.date.accepted2019-12-13
rioxxterms.funderJiscen
rioxxterms.identifier.projectUOW16012020AAen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2020-01-16en
dc.source.beginpage1
dc.source.endpage1
dc.description.versionPublished version
refterms.dateFCD2020-01-16T10:09:43Z
refterms.versionFCDVoR
refterms.dateFOA2020-01-16T10:14:55Z


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