Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering

Multiple Rotation-Based Transformation (MRBT) was introduced recently for mitigating the Apriori-Knowledge Independent Component Analysis (AK-ICA) attack on Rotation-Based Transformation (RBT), which is used for privacy-preserving data clustering. MRBT is shown to mitigate the AK-ICA attack but at the expense of data utility by not enabling conventional clustering. In this paper, the authors extend the MRBT scheme and introduce an Augmented Rotation-Based Transformation (ARBT) scheme that utilizes linearity of transformation and that both mitigates the AK-ICA attack and enables conventional clustering on data subsets transformed using the MRBT.