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Journal Article Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
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Authors
Dowon Hong, Abedelaziz Mohaisen
Issue Date
2010-06
Citation
ETRI Journal, v.32, no.3, pp.351-361
ISSN
1225-6463
Publisher
한국전자통신연구원 (ETRI)
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.4218/etrij.10.0109.0333
Project Code
09MS1200, Development of next generation security technology, Cho Hyun Sook
Abstract
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.
KSP Keywords
Computational feasibility, Data clustering, Data utility, Independent Component analysis, Privacy-preserving, clustering based, data transformation