Open Access

ARTICLE

Slicing-Based Enhanced Method for Privacy-Preserving in Publishing Big Data

Mohammed BinJubier¹, Mohd Arfian Ismail¹, Abdulghani Ali Ahmed^2,*, Ali Safaa Sadiq³
1 Faculty of Computing, Universiti Malaysia Pahang, Kuantan, Pahang, Malaysia
2 School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, United Kingdom
3 School of Engineering, Computing and Mathematical Sciences, University of Wolverhampton, Wulfruna Street Wolverhampton, WV1 1LY, United Kingdom
* Corresponding Author: Abdulghani Ali Ahmed. Email:

Computers, Materials & Continua 2022, 72(2), 3665-3686. https://doi.org/10.32604/cmc.2022.024663

Received 26 October 2021; Accepted 12 January 2022; Issue published 29 March 2022

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.

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Keywords

Big data; big data privacy preservation; anonymization; data publishing

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