A privacy preserving clustering technique using Haar wavelet transform and scaling data perturbation

Abstract

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 introduce a novel approach for privacy preserving clustering (PPC) over centralized data. The proposed technique uses Haar wavelet transform (HWT) and scaling data perturbation (SDP) to protect the underlying numerical attribute values subjected to clustering analysis. In addition, some experimental results are presented, which demonstrate that the proposed technique is effective and finds an optimum in the tradeoff between clustering utility and data privacy.