A model for Privacy Preserving in Data Mining using Soft Computing Techniques

Data mining is branch of computer science that delivers valuable information hidden in large volumes of data. The success of data mining depends on the quality of data and the algorithms used to extract information. A large number of tools and techniques have been developed for the purpose. Soft Computing methods have also emerged as a powerful tool for data mining as soft computing is tolerant to uncertainty, partial truth and imprecision. It helps in achieving solutions that are low cost, robust and tractable. Neural Networks are being extensively used for analysis purposes in every field of life from business to health sectors. In the current scenario where privacy of an individual is an important issue, people are reluctant to share their confidential information. Thereby privacy preserving in data mining (PPDM) has emerged as an indistinguishable component of data mining. The aim of this paper is to propose a model that preserves the privacy of individuals without affecting the final results of the Neural Networks.