An anonymization technique using intersected decision trees

Data mining plays an important role in analyzing the massive amount of data collected in today’s world. However, due to the public’s rising awareness of privacy and lack of trust in organizations, suitable Privacy Preserving Data Mining (PPDM) techniques have become vital. A PPDM technique provides individual privacy while allowing useful data mining. We present a novel noise addition technique called Forest Framework, two novel data quality evaluation techniques called EDUDS and EDUSC, and a security evaluation technique called SERS. Forest Framework builds a decision forest from a dataset and preserves all the patterns (logic rules) of the forest while adding noise to the dataset. We compare Forest Framework to its predecessor, Framework, and another established technique, GADP. Our comparison is done using our three evaluation criteria, as well as Prediction Accuracy. Our experimental results demonstrate the success of our proposed extensions to Framework and the usefulness of our evaluation criteria