Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches
The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and per...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-10-01
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| Series: | Digital Communications and Networks |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352864822002000 |
| _version_ | 1797640250206453760 |
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| author | Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon |
| author_facet | Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon |
| author_sort | Tehsin Kanwal |
| collection | DOAJ |
| description | The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility. |
| first_indexed | 2024-03-11T13:29:12Z |
| format | Article |
| id | doaj.art-20cce5af23254c9ab6748f1ccf14af2a |
| institution | Directory Open Access Journal |
| issn | 2352-8648 |
| language | English |
| last_indexed | 2024-03-11T13:29:12Z |
| publishDate | 2023-10-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Communications and Networks |
| spelling | doaj.art-20cce5af23254c9ab6748f1ccf14af2a2023-11-03T04:15:11ZengKeAi Communications Co., Ltd.Digital Communications and Networks2352-86482023-10-019511311140Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breachesTehsin Kanwal0Hasina Attaullah1Adeel Anjum2Abid Khan3Gwanggil Jeon4Department of Computer Science, COMSATS University Islamabad, PakistanDepartment of Computer Science, COMSATS University Islamabad, PakistanDepartment of Information Technology, Quaid-e-Azam University Islamabad, PakistanCollege of Science and Engineering, School of Computing and Maths, University of Derby, DE22 1GB, UKDepartment of Embedded Systems Engineering, Incheon National University, South Korea; Corresponding author.The inability of traditional privacy-preserving models to protect multiple datasets based on sensitive attributes has prompted researchers to propose models such as SLOMS, SLAMSA, (p, k)-Angelization, and (p, l)-Angelization, but these were found to be insufficient in terms of robust privacy and performance. (p, l)-Angelization was successful against different privacy disclosures, but it was not efficient. To the best of our knowledge, no robust privacy model based on fuzzy logic has been proposed to protect the privacy of sensitive attributes with multiple records. In this paper, we suggest an improved version of (p, l)-Angelization based on a hybrid AI approach and privacy-preserving approach like Generalization. Fuzz-classification (p, l)-Angel uses artificial intelligence based fuzzy logic for classification, a high-dimensional segmentation technique for segmenting quasi-identifiers and multiple sensitive attributes. We demonstrate the feasibility of the proposed solution by modelling and analyzing privacy violations using High-Level Petri Nets. The results of the experiment demonstrate that the proposed approach produces better results in terms of efficiency and utility.http://www.sciencedirect.com/science/article/pii/S2352864822002000GeneralizationFuzzy-logicMSAPrivacy disclosuresMembership function(p, l)-Angelization |
| spellingShingle | Tehsin Kanwal Hasina Attaullah Adeel Anjum Abid Khan Gwanggil Jeon Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches Digital Communications and Networks Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p, l)-Angelization |
| title | Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches |
| title_full | Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches |
| title_fullStr | Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches |
| title_full_unstemmed | Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches |
| title_short | Fuzz-classification (p, l)-Angel: An enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches |
| title_sort | fuzz classification p l angel an enhanced hybrid artificial intelligence based fuzzy logic for multiple sensitive attributes against privacy breaches |
| topic | Generalization Fuzzy-logic MSA Privacy disclosures Membership function (p, l)-Angelization |
| url | http://www.sciencedirect.com/science/article/pii/S2352864822002000 |
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