BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication
Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs)...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-01-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2073-431X/6/1/1 |
| _version_ | 1817993078065070080 |
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| author | Adeel Anjum Guillaume Raschia |
| author_facet | Adeel Anjum Guillaume Raschia |
| author_sort | Adeel Anjum |
| collection | DOAJ |
| description | Privacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions. |
| first_indexed | 2024-04-14T01:34:33Z |
| format | Article |
| id | doaj.art-2a9c7f3dc6564aa5ae56a1879764c20f |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-04-14T01:34:33Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-2a9c7f3dc6564aa5ae56a1879764c20f2022-12-22T02:20:01ZengMDPI AGComputers2073-431X2017-01-0161110.3390/computers6010001computers6010001BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data PublicationAdeel Anjum0Guillaume Raschia1Department of Computer Science, Comsats Institute of Information Technology, Park Road Chak Shahzad, 44000 Islamabad, PakistanLaboratoire LINA, Ecole Polytechnique, University of Nantes, 44306 Nantes, FrancePrivacy-Preserving Data Publishing (PPDP) has become a critical issue for companies and organizations that would release their data. k-Anonymization was proposed as a first generalization model to guarantee against identity disclosure of individual records in a data set. Point access methods (PAMs) are not well studied for the problem of data anonymization. In this article, we propose yet another approximation algorithm for anonymization, coined BangA, that combines useful features from Point Access Methods (PAMs) and clustering. Hence, it achieves fast computation and scalability as a PAM, and very high quality thanks to its density-based clustering step. Extensive experiments show the efficiency and effectiveness of our approach. Furthermore, we provide guidelines for extending BangA to achieve a relaxed form of differential privacy which provides stronger privacy guarantees as compared to traditional privacy definitions.http://www.mdpi.com/2073-431X/6/1/1data privacygeneralizationk-anonymitydifferential privacyBang file |
| spellingShingle | Adeel Anjum Guillaume Raschia BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication Computers data privacy generalization k-anonymity differential privacy Bang file |
| title | BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication |
| title_full | BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication |
| title_fullStr | BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication |
| title_full_unstemmed | BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication |
| title_short | BangA: An Efficient and Flexible Generalization-Based Algorithm for Privacy Preserving Data Publication |
| title_sort | banga an efficient and flexible generalization based algorithm for privacy preserving data publication |
| topic | data privacy generalization k-anonymity differential privacy Bang file |
| url | http://www.mdpi.com/2073-431X/6/1/1 |
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