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-...

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Main Authors:	Zouinina Sarah, Bennani Younès, Rogovschi Nicoleta, Lyhyaoui Abdelouahid
Format:	article
Language:	EN
Published:	De Gruyter 2020
Subjects:	microaggregation k-anonymity collaborative topological clustering 68t05 68t30 Science Q Electronic computers. Computer science QA75.5-76.95
Online Access:	https://doaj.org/article/2a2aed2e988a4d69b32cce847d3eb41a
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spelling	oai:doaj.org-article:2a2aed2e988a4d69b32cce847d3eb41a2021-12-05T14:10:51ZData Anonymization through Collaborative Multi-view Microaggregation2191-026X10.1515/jisys-2020-0026https://doaj.org/article/2a2aed2e988a4d69b32cce847d3eb41a2020-10-01T00:00:00Zhttps://doi.org/10.1515/jisys-2020-0026https://doaj.org/toc/2191-026XThe 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.Zouinina SarahBennani YounèsRogovschi NicoletaLyhyaoui AbdelouahidDe Gruyterarticlemicroaggregationk-anonymitycollaborative topological clustering68t0568t30ScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 327-345 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	microaggregation k-anonymity collaborative topological clustering 68t05 68t30 Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	microaggregation k-anonymity collaborative topological clustering 68t05 68t30 Science Q Electronic computers. Computer science QA75.5-76.95 Zouinina Sarah Bennani Younès Rogovschi Nicoleta Lyhyaoui Abdelouahid Data Anonymization through Collaborative Multi-view Microaggregation
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.
format	article
author	Zouinina Sarah Bennani Younès Rogovschi Nicoleta Lyhyaoui Abdelouahid
author_facet	Zouinina Sarah Bennani Younès Rogovschi Nicoleta Lyhyaoui Abdelouahid
author_sort	Zouinina Sarah
title	Data Anonymization through Collaborative Multi-view Microaggregation
title_short	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_sort	data anonymization through collaborative multi-view microaggregation
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/2a2aed2e988a4d69b32cce847d3eb41a
work_keys_str_mv	AT zouininasarah dataanonymizationthroughcollaborativemultiviewmicroaggregation AT bennaniyounes dataanonymizationthroughcollaborativemultiviewmicroaggregation AT rogovschinicoleta dataanonymizationthroughcollaborativemultiviewmicroaggregation AT lyhyaouiabdelouahid dataanonymizationthroughcollaborativemultiviewmicroaggregation
_version_	1718371662593785856

Data Anonymization through Collaborative Multi-view Microaggregation

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