Wavelet-Based Data Distortion for Simultaneous Privacy-Preserving and Statistics-Preserving

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 class of novel privacy-preserving data distortion methods in collaborative analysis situations based on wavelet transformation, to keep the data privacy and data statistics and data mining utilities at the same time. We further propose a multi-basis wavelet data distortion strategy for better privacy preserving in these situations. Our mathematical and experimental prove that the method can keep the distance before and after changing and it can minimize the data changing under the circumstances of keeping the statistics. Through experiments on real-life datasets, we conclude that this method is a very new promising privacy-preserving and statistics-preserving technique.