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학술지 Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
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저자
홍도원, Abedelaziz Mohaisen
발행일
201006
출처
ETRI Journal, v.32 no.3, pp.351-361
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.10.0109.0333
협약과제
09MS1200, 차세대 시큐리티 기술 개발, 조현숙
초록
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.
키워드
Data clustering, Measurements, Privacy preservation, Rotation-based transformation
KSP 제안 키워드
Computational feasibility, Data clustering, Data utility, Independent Component analysis, Privacy-preserving, clustering based, data transformation, privacy Preservation