Wavelet-Based Data Distortion for Privacy-Preserving Collaborative Analysis
Lian Liu, Jie Wang, Zhenmin Lin, Jun Zhang · 2007
With the rapid development of modern data collection and data warehouse technologies, data mining is becoming more and more a standard practice. Accompanying this trend, 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 the collaborative analysis situations base d on wavelet transformation, which provides an effective and efficient balance between data utilities and privacy protection beyond its fast run time. We also provide a new privacy breach algorithm in the collaborative analysis which could threaten the data privacy, even with the distorted data values, in the single basis wavelet transformation case. Thus, we further propose a multi-basis wavelet data distortion strategy for better privacy preser ving in these situations. Through experiments on real-life datasets, we conclude that the multi-basis wavelet data distortion method is a very promising privacy-preserving technique.