Privacy Protection by Matrix Transformation

Privacy preserving is indispensable in data mining. In this paper, we present a novel clustering method for distributed multi-party data sets using orthogonal transformation and data randomization techniques. Our method can not only protect privacy in face of collusion, but also achieve a higher level of accuracy compared to the existing methods.

[1]  Philip S. Yu,et al.  A General Survey of Privacy-Preserving Data Mining Models and Algorithms , 2008, Privacy-Preserving Data Mining.

[2]  Shangteng Huang,et al.  Privacy Preserving Clustering for Multi-party , 2007, DASFAA.

[3]  Philip S. Yu,et al.  A Condensation Approach to Privacy Preserving Data Mining , 2004, EDBT.

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