Knowledge Reserving in Privacy Preserving Data Mining
Weijia Yang · 2008
We present in this paper a novel method to protect data privacy in data mining. Nowadays, privacy is becoming an increasingly important issue in many data mining applications. Among the current privacy preserving techniques, data anonymization provides a simple and effective way to protect the sensitive data. However, in most of the related algorithms, data details are lost and the result dataset is far less informative than the original one. In our method, we adopt a statistical way to anonymize the dataset and we are able to preserve not only the data details but also the useful data knowledge. We also analyze in detail the accuracy and the privacy levels of our method. Experimental results further demonstrate the effectiveness of our method by comparing it to the existing methods.