08-01-2014, 12:52 PM
Four Groups Randomized Response Techniques in using Privacy Preserving Data Mining
Abstract
Data mining is a process in which data is combined from many sources and then produce it in useful information. Data mining is also known as knowledge discovery in database (KDD).Privacy and accuracy are the important topics in data mining when data is shared. A fruitful direction for future data mining research will be the development of techniques 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 developed for the purpose of protecting surveys privacy and avoiding answers bias mainly. In this RR technique it adds certain degree of randomness to the answer to prevent the data. The objective of our paper is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm we have considered random attributes a,b,c,d. after that we have performed the randomization on every dataset according to the values of theta. After that we applied ID3 algorithm on the randomized data. Then we calculated the gains and compared the randomized data with original data to test the accuracy level.
Abstract
Data mining is a process in which data is combined from many sources and then produce it in useful information. Data mining is also known as knowledge discovery in database (KDD).Privacy and accuracy are the important topics in data mining when data is shared. A fruitful direction for future data mining research will be the development of techniques 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 developed for the purpose of protecting surveys privacy and avoiding answers bias mainly. In this RR technique it adds certain degree of randomness to the answer to prevent the data. The objective of our paper is to enhance the privacy level in RR technique using four group schemes. First according to the algorithm we have considered random attributes a,b,c,d. after that we have performed the randomization on every dataset according to the values of theta. After that we applied ID3 algorithm on the randomized data. Then we calculated the gains and compared the randomized data with original data to test the accuracy level.