Revisiting "privacy preserving clustering by data transformation".
Revisiting "privacy preserving clustering by data transformation".
Author(s): OLIVEIRA, S. R. de M.; ZAÏANE, O.
Summary: Preserving the privacy of individuals when data are shared for clustering is a complex problem. The challenge is how to protect the underlying data values subjected to clustering without jeopardizing the similarity between objects under analysis. In this short paper, we revisit a family of geometric data transformation methods (GDTMs) that distort numerical attributes by translations, scalings, rotations, or even by the combination of these geometric transformations. Such a method was designed to address privacy-preserving clustering, in scenarios where data owners must not only meet privacy requirements but also guarantee valid clustering results. We offer a detailed, comprehensive and up-to-date picture of methods for privacy-preserving clustering by data transformation.
Publication year: 2010
Types of publication: Journal article
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