01419naa a2200193 a 450000100080000000500110000800800410001910000260006024500940008626000090018052007880018965000260097765300150100365300200101865300400103865300340107870000160111277300970112818638282013-04-05       2010    bl uuuu     u00u1 u    #d1 aOLIVEIRA, S. R. de M.  aRevisiting "privacy preserving clustering by data transformation".h[electronic resource]  c2010  aPreserving 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.  aInformation retrieval  aClustering  aClusterização  aPrivacidade em mineração de dados  aRecuperação da informação1 aZAÏANE, O.  tJournal of Information and Data Management, Belo Horizontegv. 1, n. 1, p. 53-56, Feb. 2010.