Revisiting "privacy preserving clustering by data transformation".

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

Toward standardization in privacy-preserving data mining.

Introduction. Problems in defining privacy. Privacy-preserving data mining. Privacy violation in data mining. Defining privacy preservation in data mining. Characterizing scenarios in PPDM. Principles and policies for PPDM. The OECD privacy guidelines. The implications of the OECD privacy guidelines in PPDM. Adopting PPDM policies from the OECD privacy guidelines. Requirements for PPDM. Requirements for the development of technical solutions. Requirements to guide the deployment of technical solutions. Related work. Conclusions.Na publicação: Stanley R. M. Oliveira

Privacy preserving clustering by data transformation.

Related work. Basic concepts. The basics of data perturbation. The basics of imaging geometry. The family of geometric data transformation methods. Basic definitions. The translation data perturbation method. The scaling data perturbation method. The rotation data perturbation method. The hybrid data perturbation method. Experimental results. Methodology. Measuring effectiveness. Quantifying privacy. Improving privacy. Conclusions.SBBD 2003. Na publicação: Stanley R. M. Oliveira