Data mining techniques are able to derive highly sensitive knowledge from unclassified data that is not even known to database holders. Usually, data mining contains the secured information such as financial and healthcare records. To handle such large private database with, data mining algorithms with privacy is required. The privacy preserving becomes important concern when we dealing security related data. Data perturbation is one of the well known methods for avoiding such kinds of privacy leakage. The objective of data perturbation method is to distort the individual data values while preserving the underlying statistical distribution properties. These data perturbation methods are assessed in terms of both their privacy parameters as well as its associated utility measure. Privacy parameters are used to measure the degree of privacy protection while data utility measures assess whether the dataset keeps the performance of data mining techniques after the data distortion. In this paper we present wavelet transformation for data perturbation. The experimental results show that wavelet transformation is a very promising data perturbation method.