Secure Computation in Privacy Preserving Data Mining

Abstract

Data mining is a process in which data collected from different sources is analyzed for useful information. Because data mini ng tools provides a base for upcoming trends and reactions by reading through databases for secret patterns, they allow organizations to make proactive, knowledge - driven actions and the problems that were previously too much time - consuming to resolve. Data mining software is one of a number of analytical tools for analyzing data. In the field of data mining the Privacy is most important issue when data is shared. A fruitful direction for future trends of data mining research will be the enhancement of methods that incorporate privacy concerns. Most of the methods use random permutation techniques to mask the data, for preserving the privacy of sensitive data. Randomize response techniques were dev eloped for the purpose of protecting surveys privacy and avoiding g biased answers. The proposed work is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm random attributes a, b, c, d were considered, then the randomization hav e been performed on every dataset accordi ng to the values of theta. Then ID3 and CART algorithm are applied on the randomized data. The result shows that by increasing the group, the privacy level will increase. This work shows that as compared with three group scheme with four groups scheme the accuracy decreases 6% but the privacy increases 65%