A Review on Privacy Preserving Data Mining

Most data mining applications operate under the assumption that all the data is available at a single central repository, called a data warehouse. This poses a huge privacy problem because violating only a single repository’s security exposes all the data. Although people might trust some entities with some of their data, they don’t trust anyone with all their data. With the extensive amount of data stored in

databases and other repositories it is very important to develop a powerful and effective mean for analysis and interpretation of such data for extracting the interesting and useful knowledge that could help in decision making. Data mining is such a technique which extracts the useful information from the large

repositories. Knowledge discovery in database (KDD) is another name of data mining. Privacy preserving data mining techniques are introduced with the aim of extract the relevant knowledge from the large amount of data while protecting the sensible information at the same time. In this paper we review on the various privacy preserving data mining techniques like data modification and secure multiparty computation based on the different aspects. We also analyze the comparative study of all

Techniques followed by the future research work