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Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
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  • Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
  • Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
저자명
Hong. Do-Won,Mohaisen. Abedelaziz
간행물명
ETRI journal
권/호정보
2010년|32권 3호|pp.351-361 (11 pages)
발행정보
한국전자통신연구원
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

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.