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

BinJubier, M, Arfian Ismail, M, Ali Ahmed, A and Sadiq, AS ORCID: https://orcid.org/0000-0002-5746-0257, 2022. Slicing-based enhanced method for privacy-preserving in publishing big data. Computers, Materials and Continua, 72 (2), pp. 3665-3686. ISSN 1546-2218

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Abstract

Publishing 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.

Item Type:	Journal article
Publication Title:	Computers, Materials and Continua
Creators:	BinJubier, M., Arfian Ismail, M., Ali Ahmed, A. and Sadiq, A.S.
Publisher:	Computers, Materials and Continua (Tech Science Press)
Date:	2022
Volume:	72
Number:	2
ISSN:	1546-2218
Identifiers:	Number Type 10.32604/cmc.2022.024663 DOI 1597427 Other
Rights:	This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Linda Sullivan
Date Added:	20 Sep 2022 13:18
Last Modified:	20 Sep 2022 13:18
URI:	https://irep.ntu.ac.uk/id/eprint/47053

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