Achieving Privacy Preservation when Sharing Data for Clustering

In this paper, we address the problem of protecting the underlying attribute values when sharing data for clustering. The challenge is how to meet privacy requirements and guarantee valid clustering results as well. 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 efficient and accurate; and d) it does not rely on intractability hypotheses from algebra and does not require CPU-intensive operations. We show analytically that although the data are transformed to achieve privacy, we can also get accurate clustering results by the safeguard of the global distances between data points.