Publication:
Privacy-Preserving Mechanism for Data Analytics

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dc.citedby0
dc.contributor.authorAnuar N.B.K.en_US
dc.contributor.authorBakar A.B.A.en_US
dc.contributor.authorBakar A.B.A.en_US
dc.contributor.authorid57220805366en_US
dc.contributor.authorid35178991300en_US
dc.contributor.authorid57296095700en_US
dc.date.accessioned2024-10-14T03:21:56Z
dc.date.available2024-10-14T03:21:56Z
dc.date.issued2023
dc.description.abstractThis paper proposed a mechanism to maintain the data subject�s privacy while performing analytics on electricity billing data. First, this paper implemented privacy-preserving mechanisms such as generalisation, group shuffling, suppression and full masking in a mocked electricity billing dataset. This paper then calculates the data utility metric to prove that the data is adequately preserved. Finally, the data utility of the preserved data is evaluated to ensure the preserved data is still usable to perform analytics tasks. Among the three mechanisms examined in this article, the group shuffling mechanism achieved the most outstanding visibilityen_US
dc.description.abstracthence, it is the most suitable mechanism to be used in data analytics. Apart from that, group shuffling generates a very little loss of information. � 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-981-19-2397-5_61
dc.identifier.epage691
dc.identifier.scopus2-s2.0-85136927235
dc.identifier.spage683
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85136927235&doi=10.1007%2f978-981-19-2397-5_61&partnerID=40&md5=6186dc9291460c829f30a35d3795c336
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34710
dc.identifier.volume465
dc.pagecount8
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.subjectData analytics
dc.subjectData utility
dc.subjectEnergy data
dc.subjectPDPA
dc.subjectPrivacy
dc.subjectPrivacy metrics
dc.titlePrivacy-Preserving Mechanism for Data Analyticsen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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