상세정보
학술지
Augmented Rotation-Based Transformation for Privacy-Preserving Data Clustering
- 저자
- 홍도원, Abedelaziz Mohaisen
- 발행일
- 201006
- 출처
- ETRI Journal, v.32 no.3, pp.351-361
- ISSN
- 1225-6463
- 출판사
- 한국전자통신연구원 (ETRI)
- 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.
- KSP 제안 키워드
- Clustering-Based, Computational feasibility, Data clustering, Data utility, Independent Component analysis, Privacy-preserving, data transformation