Soft Computing Techniques for Privacy Preserving Data Mining

Nowadays, a vast amount of data is being collected from various organizations like healthcare, marketing and insurance. Processing such data has raised serious issues related in order to protect the individual data privacy like ethical, philosophical and legal. To ensure the privacy of the individual, in the literature privacy preserving data mining (PPDM) techniques are available to protect the data and mining results. In this study, the different Soft Computing (SC) techniques with and without anonymized dataset using data mining tool WEKA is presented. The aim of this study is to investigate the performance of different soft computing techniques for the diabetic dataset and to compare the efficiency of privacy preserving data mining. The accuracy of soft computing (SC) techniques is evaluated using Fuzzy Systems, Evolutionary computing, Neural Networks, Machine Learning and probabilistic Reasoning.