A privacy preserving clustering technique using hybrid data transformation method
Liming Li, Zhang Qishan · 2009
Despite many successful stories of data mining in a wide range of applications, this technique has raised some issues related to privacy and security of individuals. Due to these issues, data owners are often unwilling to share their sensitive information with data miners. In this paper, we present a novel method for privacy preserving clustering over centralized data. The proposed method is built upon the application of Double-Reflecting Data Perturbation Method (DRDP) and Rotation Based Translation (RBT) in order to provide secrecy of confidential numerical attributes without losing accuracy in results. The experiments demonstrate that the proposed method is effective and provides a feasible approach to balancing privacy and accuracy.