Abstract

Abstract. 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.

The sharing of association rules has been proven beneficial in business collaboration, but requires privacy safeguards. One may decide to disclose only part of the knowledge and conceal strategic patterns called sensitive rules. These sensitive rules must be protected before sharing since they are paramount for strategic decisions and need to remain private. Some companies prefer to share their data for collaboration, while others prefer to share only the patterns discovered from their data. The challenge here is how to protect the sensitive rules without putting at risk the effectiveness of data mining per se. To address this challenging problem, we propose a unified framework which combines techniques for efficiently hiding sensitive rules: a set of algorithms to protect sensitive knowledge in transactional databases; retrieval facilities to speed up the process of protecting sensitive knowledge; and a set of metrics to evaluate the effectiveness of the proposed algorithms in terms of information loss and to quantify how much private information has been disclosed. Our experiments demonstrate that our framework is effective and achieves significant improvement over the other approaches presented in the literature.