Data Anonymization through Collaborative Multi-view Microaggregation
| Autor: | Sarah Zouinina, Younès Bennani, Abdelouahid Lyhyaoui, Nicoleta Rogovschi |
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| Rok vydání: | 2020 |
| Předmět: |
Data anonymization
68t30 Computer science Science k-anonymity 020206 networking & telecommunications QA75.5-76.95 02 engineering and technology 68t05 computer.software_genre collaborative topological clustering Artificial Intelligence Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing microaggregation Data mining computer Software Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 30, Iss 1, Pp 327-345 (2020) |
| ISSN: | 2191-026X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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