Revisiting "privacy preserving clustering by data transformation".
No hay miniatura disponible
Fecha
Autores
Título de la revista
ISSN de la revista
Título del volumen
Editor
Resumen
Descripción
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.
Palabras clave
Clusterização, Privacidade em mineração de dados, Recuperação da informação, Clustering, Information retrieval