Data Perturbation by Rotation for Privacy-Preserving Clustering
Stanley Robson de Medeiros Oliveira, Osmar R. Zai͏̈ane · 2004
Preserving privacy of individuals when data are shared for clustering is a complex problem. The challenge is how to protect the underlying attribute values subjected to clustering without jeopardizing the similarity between data objects under analysis. To address this problem, data owners must not only meet privacy requirements but also guarantee valid clustering results. To achieve this dual goal, we propose a novel spatial data transformation method called Rotation-Based Transformation (RBT). The major features of our data transformation are: a) it is independent of any clustering algorithm, b) it has a sound mathematical foundation; c) it is ecient and accurate; and d) it does not rely on intractability hypotheses from algebra and does not require CPU-intensive operations.