Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes
The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the...
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9732456/ |
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| author | J. Jayapradha M. Prakash Youseef Alotaibi Osamah Ibrahim Khalaf Saleh Ahmed Alghamdi |
| author_facet | J. Jayapradha M. Prakash Youseef Alotaibi Osamah Ibrahim Khalaf Saleh Ahmed Alghamdi |
| author_sort | J. Jayapradha |
| collection | DOAJ |
| description | The publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the correlation among the attributes, which in turn leads to much utility loss. An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes. The Heap Bucketization-anonymity model used anatomization to vertically partition the dataset into 1. Quasi-identifier table and 2. Sensitive attribute table. The quasi-identifier is anonymized by implementing k-anonymity and slicing and the sensitive attributes are anonymized by applying slicing and Heap Bucketization. The metrics Normalized Certainty Penalty and KL-divergence have been used to compute the utility loss in the patient dataset. The experimental results show that the HB-anonymity can significantly achieve high privacy with less utility loss than other existing models. The HB-anonymity model not only balances the utility and privacy also eradicates the i) background knowledge attack, ii) quasi-identifier attack iii) membership attack, iv) non-membership attack and v) fingerprint correlation attack. |
| format | Article |
| id | doaj-art-71ec7c11bd6a43b0a3ef4fb32028c2ab |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-71ec7c11bd6a43b0a3ef4fb32028c2ab2025-08-20T01:48:20ZengIEEEIEEE Access2169-35362022-01-0110287732879110.1109/ACCESS.2022.31583129732456Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive AttributesJ. Jayapradha0https://orcid.org/0000-0002-2548-9135M. Prakash1https://orcid.org/0000-0001-8008-4424Youseef Alotaibi2https://orcid.org/0000-0002-0840-1867Osamah Ibrahim Khalaf3https://orcid.org/0000-0002-4750-8384Saleh Ahmed Alghamdi4Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, IndiaDepartment of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, IndiaDepartment of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi ArabiaAl-Nahrain Nanorenewable Energy Research Center, Al-Nahrain University, Baghdad, IraqDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaThe publication of a patient’s dataset is essential for various medical investigations and decision-making. Currently, significant focus has been established to protect privacy during data publishing. The existing privacy models for multiple sensitive attributes do not concentrate on the correlation among the attributes, which in turn leads to much utility loss. An efficient model Heap Bucketization-anonymity (HBA) has been proposed to balance privacy and utility with multiple sensitive attributes. The Heap Bucketization-anonymity model used anatomization to vertically partition the dataset into 1. Quasi-identifier table and 2. Sensitive attribute table. The quasi-identifier is anonymized by implementing k-anonymity and slicing and the sensitive attributes are anonymized by applying slicing and Heap Bucketization. The metrics Normalized Certainty Penalty and KL-divergence have been used to compute the utility loss in the patient dataset. The experimental results show that the HB-anonymity can significantly achieve high privacy with less utility loss than other existing models. The HB-anonymity model not only balances the utility and privacy also eradicates the i) background knowledge attack, ii) quasi-identifier attack iii) membership attack, iv) non-membership attack and v) fingerprint correlation attack.https://ieeexplore.ieee.org/document/9732456/Privacy-preservinganatomizationheap bucketizationPearson correlationk-anonymityslicing |
| spellingShingle | J. Jayapradha M. Prakash Youseef Alotaibi Osamah Ibrahim Khalaf Saleh Ahmed Alghamdi Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes IEEE Access Privacy-preserving anatomization heap bucketization Pearson correlation k-anonymity slicing |
| title | Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes |
| title_full | Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes |
| title_fullStr | Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes |
| title_full_unstemmed | Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes |
| title_short | Heap Bucketization Anonymity—An Efficient Privacy-Preserving Data Publishing Model for Multiple Sensitive Attributes |
| title_sort | heap bucketization anonymity x2014 an efficient privacy preserving data publishing model for multiple sensitive attributes |
| topic | Privacy-preserving anatomization heap bucketization Pearson correlation k-anonymity slicing |
| url | https://ieeexplore.ieee.org/document/9732456/ |
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