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With the rapid development of data mining technologies, preserving privacy in certain data becomes a challenge to data mining applications in many fields, especially in medical, financial and homeland security fields. We present a privacy-preserving strategy based on wavelet perturbation to keep the data privacy and data statistical properties and data mining utilities at the same time. Our mathematical analyses and experimental results show that this method can keep the distance before and after perturbation and it can preserve the basic statistical properties of the original data while maximizing the data utilities. Through experiments on real-life datasets, we conclude that this method is a promising privacy-preserving and statistics-preserving technique.