Privacy Preserving Clustering by Isometric Transformation

This paper is concentrated on the issue of protecting the underlying attribute values when sharing data for clustering and proposes a method called Isometric-Based Transformation(IBT).IBT selects the attribute pairs and then distorts them with isometric transformation.In the process of transformation,the goal is to find the proper angle ranges to satisfy the least privacy preserving requirement and then randomly choose one angle θ in this interval.The experiments demonstrate that the method efficiently distorts attribute values,preserves privacy information and guarantees valid clustering results.