A privacy preserving clustering technique for horizontally and vertically distributed datasets

摘要

Despite the benefits of data mining in a wide range of applications, this technique has raised some issues related to the privacy and security of individuals. Due to these issues, data owners may prevent to share their sensitive information with data miners. On the other hand, in distributed environments, other issues related to the distribution of data will raise, which will make the preservation of privacy more challengeable. To solve these problems, different privacy preserving data mining (PPDM) techniques have been introduced. In this paper, a new privacy preserving clustering (PPC) technique for horizontally and vertically distributed datasets is proposed. The proposed technique uses Haar wavelet transforms (HWT) and scaling data perturbation (SDP) to achieve both data hiding and data reduction for protecting private numerical attribute values in distributed datasets. The results of our evaluations demonstrated that the proposed technique provides a proper degree of privacy and quality of clustering for distributed datasets and also runs fast. Our experiments have also shown that the proposed technique provides better privacy and clustering results comparing to the other existing privacy preserving clustering techniques applicable to distributed datasets. The proposed algorithms and the results of their experimental evaluations using different datasets are presented in this paper.