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On the Use of Haar Wavelet Transform and Scaling Data perturbation for Privacy Preserving Clustering of Large Datasets

In recent years, data mining has raised some issues related to the privacy of individuals. Due to these issues, data owners abstain to share their sensitive information with data miners. Thus, privacy preserving data mining (PPDM) techniques have been introduced. One of these techniques is for data hiding purpose, which depending on the type of privacy problems can be categorized as follows: (1) Perturbation of the original sensitive data before delivering to the data miners and (2) anonymization of the entities before publishing the data. In this paper, we propose a new technique for privacy preserving clustering (PPC) over centralized databases that belongs to the first category. The proposed technique uses Haar wavelet transform and scaling data perturbation to provide both data hiding and data reduction to protect the underlying numerical attributes subjected to clustering analysis. We present extensive experimental results for the proposed technique. Our experimental evaluations demonstrated that the proposed technique is effective and find a good tradeoff between clustering quality, data privacy, and data reduction. We will present the results of the comparison of the proposed technique with other existing PPC techniques. We will also present a formal description of the proposed technique and its privacy analysis, which proves its security.