On the Use of Wavelet Transform for Privacy Preserving Data Mining

S. Asif Kabir, A.M. Youssef, and A.K. Elhakeem (Canada)


Privacy preserving data mining, DWT


Data mining is the process of automatically searching large amount of data to extract useful information and patterns using tools such as classification, association and rule mining. Data mining often involves data that contains private information such as healthcare or financial records and there has been growing concern about the chance of misusing the personal information extracted from such data. In particular, the increasing ability to trace and collect large amount of data with the use of current technology has led to an interest in the development of data mining algorithms which preserve user privacy. Data perturbation is one of the well known techniques for privacy preserving data mining. In this paper, we investigate the use of the Discrete Wavelet Transform (DWT) with truncation for data perturbation. Our experimental results show that the proposed method is effective in concealing the sensitive information while preserving the performance of data mining techniques after the data distortion.

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