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Privacy-Preserving Mechanism for Data Analytics

This 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. Th...

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Main Authors: Anuar N.B.K., Bakar A.B.A.
Other Authors: 57220805366
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2024
Subjects:
Data analytics
Data utility
Energy data
PDPA
Privacy
Privacy metrics
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id my.uniten.dspace-34710
record_format dspace
spelling my.uniten.dspace-347102024-10-14T11:21:57Z Privacy-Preserving Mechanism for Data Analytics Anuar N.B.K. Bakar A.B.A. Bakar A.B.A. 57220805366 35178991300 57296095700 Data analytics Data utility Energy data PDPA Privacy Privacy metrics This 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 visibility hence, 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. Final 2024-10-14T03:21:56Z 2024-10-14T03:21:56Z 2023 Conference Paper 10.1007/978-981-19-2397-5_61 2-s2.0-85136927235 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85136927235&doi=10.1007%2f978-981-19-2397-5_61&partnerID=40&md5=6186dc9291460c829f30a35d3795c336 https://irepository.uniten.edu.my/handle/123456789/34710 465 683 691 Springer Science and Business Media Deutschland GmbH 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/
topic Data analytics
Data utility
Energy data
PDPA
Privacy
Privacy metrics
spellingShingle Data analytics
Data utility
Energy data
PDPA
Privacy
Privacy metrics
Anuar N.B.K.
Bakar A.B.A.
Bakar A.B.A.
Privacy-Preserving Mechanism for Data Analytics
description This 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 visibility
author2 57220805366
author_facet 57220805366
Anuar N.B.K.
Bakar A.B.A.
Bakar A.B.A.
format Conference Paper
author Anuar N.B.K.
Bakar A.B.A.
Bakar A.B.A.
author_sort Anuar N.B.K.
title Privacy-Preserving Mechanism for Data Analytics
title_short Privacy-Preserving Mechanism for Data Analytics
title_full Privacy-Preserving Mechanism for Data Analytics
title_fullStr Privacy-Preserving Mechanism for Data Analytics
title_full_unstemmed Privacy-Preserving Mechanism for Data Analytics
title_sort privacy-preserving mechanism for data analytics
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2024
_version_ 1814061192415543296
score 13.214268

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