Vector Quantization for Privacy Preserving Data Mining
Huge volumes of detailed personal data are regularly collected and analyzed by applications. Such data include shopping habits, criminal records, medical history, credit records, among others. Privacy preserving data mining is becoming increasingly important issue as it predicts high sensitive information. In this paper, the authors provide new dimensions of privacy preserving data mining i.e., transformation using vector quantization. They will show reconstruction based technique for numerical data. Finally the performance of the proposed techniques is evaluated using accuracy and distortion parameters.