A Privacy Preservation Masking Method to Support Business Collaboration

Stanley R. M. Oliveira

This paper introduces a privacy preservation masking method to support business collaboration, called Dimensionality Reduction-Based Transformation (DRBT). This method relies on the intuition behind random projection to mask the underlying attribute values subject to cluster analysis. Using DRBT, data owners are able to find a solution that meets privacy requirements and guarantees valid clustering results. DRBT was validated taking into account five real datasets. The major features of this method are: a) it is independent of distance-based clustering algorithms; b) it has a sound mathematical foundation; and c) it does not require CPU-intensive operations.

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