On the comparison of some fuzzy clustering methods for privacy preserving data mining: towards the development of specific information loss measures.

*(English)*Zbl 1183.68510Summary: Policy makers and researchers require raw data collected from agencies and companies for their analysis. Nevertheless, any transmission of data to third parties should satisfy some privacy requirements in order to avoid the disclosure of sensitive information.

The areas of privacy preserving data mining and statistical disclosure control develop mechanisms for ensuring data privacy. Masking methods are one of such mechanisms. With them, third parties can do computations with a limited risk of disclosure.

Disclosure risk and information loss measures have been developed in order to evaluate in which extent data is protected and in which extent data is perturbated. Most of the information loss measures currently existing in the literature are general purpose ones (i.e., not oriented to a particular application). In this work we develop cluster specific information loss measures (for fuzzy clustering). For this purpose we study how to compare the results of fuzzy clustering. I.e., how to compare fuzzy clusters.

The areas of privacy preserving data mining and statistical disclosure control develop mechanisms for ensuring data privacy. Masking methods are one of such mechanisms. With them, third parties can do computations with a limited risk of disclosure.

Disclosure risk and information loss measures have been developed in order to evaluate in which extent data is protected and in which extent data is perturbated. Most of the information loss measures currently existing in the literature are general purpose ones (i.e., not oriented to a particular application). In this work we develop cluster specific information loss measures (for fuzzy clustering). For this purpose we study how to compare the results of fuzzy clustering. I.e., how to compare fuzzy clusters.

##### MSC:

68T05 | Learning and adaptive systems in artificial intelligence |

68T37 | Reasoning under uncertainty in the context of artificial intelligence |

68T99 | Artificial intelligence |

##### Keywords:

privacy preserving data mining; statistical disclosure control; fuzzy clustering; fuzzy \(c\)-means; fuzzy \(c\)-means with tolerance
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\textit{V. Torra} et al., Kybernetika 45, No. 3, 548--560 (2009; Zbl 1183.68510)

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