论文信息 - Mitigating the ICA Attack against Rotation Based Transformation for Privacy Preserving Clustering

Mitigating the ICA Attack against Rotation Based Transformation for Privacy Preserving Clustering

The rotation-based transformation (RBT) for privacy preserving data mining is vulnerable to the independent component analysis (ICA) attack. This paper introduces a modified multiple-rotation-based transformation technique for special mining applications, mitigating the ICA attack while maintaining the advantages of the RBT.

Aziz Mohaisen | Dowon Hong

[1] Kun Liu,et al. Random projection-based multiplicative data perturbation for privacy preserving distributed data mining , 2006, IEEE Transactions on Knowledge and Data Engineering.

[2] Dongman Lee,et al. A Semantic Service Discovery Network for Large‐Scale Ubiquitous Computing Environments , 2007 .

[3] Keke Chen,et al. Privacy preserving data classification with rotation perturbation , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[4] Keke Chen,et al. Towards Attack-Resilient Geometric Data Perturbation , 2007, SDM.

[5] Keun Ho Ryu,et al. Spatiotemporal Pattern Mining Technique for Location‐Based Service System , 2008 .

[6] Osmar R. Zaïane,et al. Achieving Privacy Preservation when Sharing Data for Clustering , 2004, Secure Data Management.

[7] Xintao Wu,et al. Deriving Private Information from Arbitrarily Projected Data , 2007, PAKDD.