Slicing-based enhanced method for privacy-preserving in publishing big data
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’ se...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Tech Science Press
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33598/ http://umpir.ump.edu.my/id/eprint/33598/1/Slicing%20based%20enhanced%20method%20for%20privacy%20preserving.pdf |
| _version_ | 1848824294983860224 |
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| author | BinJubeir, Mohammed Ma. Mohd Arfian, Ismail Ali Ahmed, Abdulghani Sadiq, Ali Safaa |
| author_facet | BinJubeir, Mohammed Ma. Mohd Arfian, Ismail Ali Ahmed, Abdulghani Sadiq, Ali Safaa |
| author_sort | BinJubeir, Mohammed Ma. |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:10:45Z |
| format | Article |
| id | ump-33598 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:10:45Z |
| publishDate | 2022 |
| publisher | Tech Science Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-335982022-03-31T01:45:54Z http://umpir.ump.edu.my/id/eprint/33598/ Slicing-based enhanced method for privacy-preserving in publishing big data BinJubeir, Mohammed Ma. Mohd Arfian, Ismail Ali Ahmed, Abdulghani Sadiq, Ali Safaa QA76 Computer software 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. Tech Science Press 2022-03 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/33598/1/Slicing%20based%20enhanced%20method%20for%20privacy%20preserving.pdf BinJubeir, Mohammed Ma. and Mohd Arfian, Ismail and Ali Ahmed, Abdulghani and Sadiq, Ali Safaa (2022) Slicing-based enhanced method for privacy-preserving in publishing big data. Computers, Materials & Continua, 72 (2). pp. 3665-3686. ISSN 1546-2226. (Published) https://doi.org/10.32604/cmc.2022.024663 https://doi.org/10.32604/cmc.2022.024663 |
| spellingShingle | QA76 Computer software BinJubeir, Mohammed Ma. Mohd Arfian, Ismail Ali Ahmed, Abdulghani Sadiq, Ali Safaa Slicing-based enhanced method for privacy-preserving in publishing big data |
| title | Slicing-based enhanced method for privacy-preserving in publishing big data |
| title_full | Slicing-based enhanced method for privacy-preserving in publishing big data |
| title_fullStr | Slicing-based enhanced method for privacy-preserving in publishing big data |
| title_full_unstemmed | Slicing-based enhanced method for privacy-preserving in publishing big data |
| title_short | Slicing-based enhanced method for privacy-preserving in publishing big data |
| title_sort | slicing-based enhanced method for privacy-preserving in publishing big data |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/33598/ http://umpir.ump.edu.my/id/eprint/33598/ http://umpir.ump.edu.my/id/eprint/33598/ http://umpir.ump.edu.my/id/eprint/33598/1/Slicing%20based%20enhanced%20method%20for%20privacy%20preserving.pdf |