Data Anonymization through Collaborative Multi-view Microaggregation
The interest in data anonymization is exponentially growing, motivated by the will of the governments to open their data. The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk. One of the most known frameworks of data anonymization is k-...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2020-10-01
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| Series: | Journal of Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/jisys-2020-0026 |
| _version_ | 1819178063084126208 |
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| author | Zouinina Sarah Bennani Younès Rogovschi Nicoleta Lyhyaoui Abdelouahid |
| author_facet | Zouinina Sarah Bennani Younès Rogovschi Nicoleta Lyhyaoui Abdelouahid |
| author_sort | Zouinina Sarah |
| collection | DOAJ |
| description | The interest in data anonymization is exponentially growing, motivated by the will of the governments to open their data. The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk. One of the most known frameworks of data anonymization is k-anonymity, this method assumes that a dataset is anonymous if and only if for each element of the dataset, there exist at least k − 1 elements identical to it. In this paper, we propose two techniques to achieve k-anonymity through microaggregation: k-CMVM and Constrained-CMVM. Both, use topological collaborative clustering to obtain k-anonymous data. The first one determines the k levels automatically and the second defines it by exploration. We also improved the results of these two approaches by using pLVQ2 as a weighted vector quantization method. The four methods proposed were proven to be efficient using two data utility measures, the separability utility and the structural utility. The experimental results have shown a very promising performance. |
| first_indexed | 2024-12-22T21:36:35Z |
| format | Article |
| id | doaj.art-2a2aed2e988a4d69b32cce847d3eb41a |
| institution | Directory Open Access Journal |
| issn | 2191-026X |
| language | English |
| last_indexed | 2024-12-22T21:36:35Z |
| publishDate | 2020-10-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Journal of Intelligent Systems |
| spelling | doaj.art-2a2aed2e988a4d69b32cce847d3eb41a2022-12-21T18:11:44ZengDe GruyterJournal of Intelligent Systems2191-026X2020-10-0130132734510.1515/jisys-2020-0026jisys-2020-0026Data Anonymization through Collaborative Multi-view MicroaggregationZouinina Sarah0Bennani Younès1Rogovschi Nicoleta2Lyhyaoui Abdelouahid3Université Sorbonne Paris Nord, LIPN UMR7030CNRS, FranceUniversité Sorbonne Paris Nord, LIPN UMR7030CNRS, FranceUniversité de Paris, LIPADE, FranceUniversité Abdelmalek Essaadi, ENSA of Tangier, LTI, MoroccoThe interest in data anonymization is exponentially growing, motivated by the will of the governments to open their data. The main challenge of data anonymization is to find a balance between data utility and the amount of disclosure risk. One of the most known frameworks of data anonymization is k-anonymity, this method assumes that a dataset is anonymous if and only if for each element of the dataset, there exist at least k − 1 elements identical to it. In this paper, we propose two techniques to achieve k-anonymity through microaggregation: k-CMVM and Constrained-CMVM. Both, use topological collaborative clustering to obtain k-anonymous data. The first one determines the k levels automatically and the second defines it by exploration. We also improved the results of these two approaches by using pLVQ2 as a weighted vector quantization method. The four methods proposed were proven to be efficient using two data utility measures, the separability utility and the structural utility. The experimental results have shown a very promising performance.https://doi.org/10.1515/jisys-2020-0026microaggregationk-anonymitycollaborative topological clustering68t0568t30 |
| spellingShingle | Zouinina Sarah Bennani Younès Rogovschi Nicoleta Lyhyaoui Abdelouahid Data Anonymization through Collaborative Multi-view Microaggregation Journal of Intelligent Systems microaggregation k-anonymity collaborative topological clustering 68t05 68t30 |
| title | Data Anonymization through Collaborative Multi-view Microaggregation |
| title_full | Data Anonymization through Collaborative Multi-view Microaggregation |
| title_fullStr | Data Anonymization through Collaborative Multi-view Microaggregation |
| title_full_unstemmed | Data Anonymization through Collaborative Multi-view Microaggregation |
| title_short | Data Anonymization through Collaborative Multi-view Microaggregation |
| title_sort | data anonymization through collaborative multi view microaggregation |
| topic | microaggregation k-anonymity collaborative topological clustering 68t05 68t30 |
| url | https://doi.org/10.1515/jisys-2020-0026 |
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