Comprehensive survey on Big Data Privacy Protection
In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolv...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019
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| Online Access: | http://umpir.ump.edu.my/id/eprint/29370/ http://umpir.ump.edu.my/id/eprint/29370/1/Comprehensive%20Survey%20on%20Big%20Data%20Privacy%20Protection.pdf |
| _version_ | 1848823263604506624 |
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| author | BinJubeir, Mohammed Ali Ahmed, Abdul Ghani Mohd Arfian, Ismail Sadiq, Ali Safaa Muhammad Khurram, Khan |
| author_facet | BinJubeir, Mohammed Ali Ahmed, Abdul Ghani Mohd Arfian, Ismail Sadiq, Ali Safaa Muhammad Khurram, Khan |
| author_sort | BinJubeir, Mohammed |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of con�dential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a speci�c approach to enable the development of a good data mining model on modi�ed data, thereby meeting a speci�ed privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classi�ed using various approaches for data modi�cation. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in
PPDM. |
| first_indexed | 2025-11-15T02:54:22Z |
| format | Article |
| id | ump-29370 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T02:54:22Z |
| publishDate | 2019 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-293702020-09-22T04:58:11Z http://umpir.ump.edu.my/id/eprint/29370/ Comprehensive survey on Big Data Privacy Protection BinJubeir, Mohammed Ali Ahmed, Abdul Ghani Mohd Arfian, Ismail Sadiq, Ali Safaa Muhammad Khurram, Khan QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) In recent years, the ever-mounting problem of Internet phishing has been threatening the secure propagation of sensitive data over the web, thereby resulting in either outright decline of data distribution or inaccurate data distribution from several data providers. Therefore, user privacy has evolved into a critical issue in various data mining operations. User privacy has turned out to be a foremost criterion for allowing the transfer of con�dential information. The intense surge in storing the personal data of customers (i.e., big data) has resulted in a new research area, which is referred to as privacy-preserving data mining (PPDM). A key issue of PPDM is how to manipulate data using a speci�c approach to enable the development of a good data mining model on modi�ed data, thereby meeting a speci�ed privacy need with minimum loss of information for the intended data analysis task. The current review study aims to utilize the tasks of data mining operations without risking the security of individuals' sensitive information, particularly at the record level. To this end, PPDM techniques are reviewed and classi�ed using various approaches for data modi�cation. Furthermore, a critical comparative analysis is performed for the advantages and drawbacks of PPDM techniques. This review study also elaborates on the existing challenges and unresolved issues in PPDM. IEEE 2019-12-25 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/29370/1/Comprehensive%20Survey%20on%20Big%20Data%20Privacy%20Protection.pdf BinJubeir, Mohammed and Ali Ahmed, Abdul Ghani and Mohd Arfian, Ismail and Sadiq, Ali Safaa and Muhammad Khurram, Khan (2019) Comprehensive survey on Big Data Privacy Protection. IEEE Access, 8. pp. 2067-2079. ISSN 2169-3536. (Published) https://ieeexplore.ieee.org/xpl https://doi.org/10.1109/ACCESS.2019.2962368 |
| spellingShingle | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) BinJubeir, Mohammed Ali Ahmed, Abdul Ghani Mohd Arfian, Ismail Sadiq, Ali Safaa Muhammad Khurram, Khan Comprehensive survey on Big Data Privacy Protection |
| title | Comprehensive survey on Big Data Privacy
Protection |
| title_full | Comprehensive survey on Big Data Privacy
Protection |
| title_fullStr | Comprehensive survey on Big Data Privacy
Protection |
| title_full_unstemmed | Comprehensive survey on Big Data Privacy
Protection |
| title_short | Comprehensive survey on Big Data Privacy
Protection |
| title_sort | comprehensive survey on big data privacy
protection |
| topic | QA Mathematics QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/29370/ http://umpir.ump.edu.my/id/eprint/29370/ http://umpir.ump.edu.my/id/eprint/29370/ http://umpir.ump.edu.my/id/eprint/29370/1/Comprehensive%20Survey%20on%20Big%20Data%20Privacy%20Protection.pdf |