08-01-2014, 12:53 PM
Framework and Classification of Privacy Preserving Data Mining
Abstract
Privacy and accuracy are the important issues 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. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. 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.
Abstract
Privacy and accuracy are the important issues 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. Privacy preserving data mining has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes .So people have become increasingly unwilling to share their data, frequently resulting in individuals either refusing to share their data or providing incorrect data. In recent years, privacy preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. 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.