  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, we
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. In order to demonstrate the computational
feasibility aspect of ARBT along with RBT and MRBT, we develop a toolkit and
use it to empirically compare the different schemes of privacy-preserving data
clustering based on data transformation in terms of their overhead and privacy.
