MARC details
| 000 -LEADER |
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| fixed length control field |
04788ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125110920.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240823t20242024my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0009936 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FKOM .M74 2024 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Mohammed Mahfoudh Khamis Binjubeir, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Enhanced slicing-based anonymization approach for privacy-preserving data publishing with improved data utility / |
| Statement of responsibility, etc. |
Mohammed Mahfoudh Khamis Binjubeir |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2024 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
Β© 2024 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xi, 100 pages : |
| Other physical details |
illustration ; |
| Materials specified |
0 cm. +30 cm. + |
| Accompanying material |
1 CD-ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Computing |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Doctor of Philosophy) -- Universiti Malaysia Pahang β 2024 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| -- |
ncludes bibliographical referencesIncludes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Data publication is a widely used method for sharing data, particularly in research fields, as it allows for data mining operations to extract valuable knowledge from published databases. This knowledge can be utilized for representation, interpretation, or the discovery of interesting patterns. However, the full potential of published partial data, derived from large datasets or a series of datasets, is yet to be realized, primarily due to various challenges faced by scholars during the extraction of knowledge from published data. One significant challenge is related to data privacy, which often results in the disclosure of individuals' identities, unauthorized access to private information, and the misuse of personal data for unintended purposes. This issue has become a major hindrance to the advancement of published data. To address these concerns and ensure data utility, several anonymization-based approaches have been developed in the field of Privacy-Preserving Data Publishing (PPDP). The effectiveness of data anonymization approaches relies on different protection methods employed to achieve privacy. However, these protection methods often either excessively falsify data or demand an impractically high level of trust in different data-sharing scenarios. Protecting private data from people who must not access this information and the individualsβ capability to determine or infer the identity of individuals who can access their personal information are crucial aspects of data protection methods. Improving protection methods for data publication is crucial to strike a balance between data utility and individuals' privacy, presenting a significant challenge. To achieve effective data anonymization, this study proposes an enhanced approach called the Upper Lower (UL) level-based protection approach, based on the slicing approach. The UL approach aims to strike a better balance between utility and privacy. The study proposes a methodology involving the division of data into horizontal and vertical partitions and leveraging the Lower Protection Level (πΏππΏ) and Upper Protection Level (πππΏ) to compute unique and identical attributes. By swapping these attributes, the published data can be effectively safeguarded against disclosure risks while still preserving adequate diversity. The key idea is to choose a set of attributes to determine the required level of protection and swap between them to improve published data privacy while preserving high data utility. The Adult dataset, which included a real dataset, was used, and according to the results, the UL approach could maintain the dataβs usefulness while offering improved privacy preservation. The proposed approach delivers about 92.47% data utility, which is more than what is achieved when the percentage of exchange level is 2% using πΏππΏ and 98% using πππΏ with a 4.5K education dataset. With a 5% swap rate, the proposed approach obtains 92.19% using πΏππΏ and 95% using πππΏ. In conclusion, the UL approach minimizes the risk of data disclosure compared to existing works such as merging, πβπ·π, Mondrian, composition, probabilistic, and hybrid methods. By employing this approach, data publication can be carried out in a manner that ensures practical usability of data while protecting individuals' privacy. Striking a balance between utility and privacy is crucial, and the UL approach offers a promising solution to achieve this balance |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Computing |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |