Despite the benefits 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 may prevent 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 on the base 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. Our experiments demonstrate that our proposed method is effective and provides acceptable values in practice for balancing privacy and accuracy.
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