Slicing-based enhanced method for privacy-preserving in publishing big data

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dc.cclicenceCC-BYen
dc.contributor.authorBinJubier, Mohammed
dc.contributor.authorIsmail, Mohd Arfian
dc.contributor.authorAhmed, Abdulghani Ali
dc.contributor.authorSadiq, Ali Safaa
dc.date.acceptance2022
dc.date.accessioned2022-04-11T12:45:47Z
dc.date.available2022-04-11T12:45:47Z
dc.date.issued2022-03-29
dc.descriptionopen access articleen
dc.description.abstractPublishing big data and making it accessible to researchers is important for knowledge building as it helps in applying highly efficient methods to plan, conduct, and assess scientific research. However, publishing and processing big data poses a privacy concern related to protecting individuals’ sensitive information while maintaining the usability of the published data. Several anonymization methods, such as slicing and merging, have been designed as solutions to the privacy concerns for publishing big data. However, the major drawback of merging and slicing is the random permutation procedure, which does not always guarantee complete protection against attribute or membership disclosure. Moreover, merging procedures may generate many fake tuples, leading to a loss of data utility and subsequent erroneous knowledge extraction. This study therefore proposes a slicing-based enhanced method for privacy-preserving big data publishing while maintaining the data utility. In particular, the proposed method distributes the data into horizontal and vertical partitions. The lower and upper protection levels are then used to identify the unique and identical attributes’ values. The unique and identical attributes are swapped to ensure the published big data is protected from disclosure risks. The outcome of the experiments demonstrates that the proposed method could maintain data utility and provide stronger privacy preservation.en
dc.funderOther external funder (please detail below)en
dc.funder.otherPostgraduate Research Grants Scheme (PGRS)en
dc.identifier.citationBinJubier, M., Ismail, M.A., Ahmed, A.A. and Sadiq, A.S. (2022) Slicing-based enhanced method for privacy-preserving in publishing big data. Computers, Materials and Continua,en
dc.identifier.doihttps://doi.org/10.32604/cmc.2022.024663
dc.identifier.issn1546-2218
dc.identifier.urihttps://hdl.handle.net/2086/21814
dc.language.isoenen
dc.peerreviewedYesen
dc.projectidPGRS190360en
dc.publisherTech Science Pressen
dc.researchinstituteCyber Technology Institute (CTI)en
dc.subjectBig dataen
dc.subjectBig data privacy preservationen
dc.subjectAnonymizationen
dc.subjectData publishingen
dc.titleSlicing-based enhanced method for privacy-preserving in publishing big dataen
dc.typeArticleen

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