Hybrid Methodology of K-Annomity and Randomization for Privacy Preserving in Data Mining

Abstract

The purpose of this research is to transform the data in original form that is in unsecured form while doing the mining procedure and protect
different attacks that involve in privacy theft and work on preserving privacy in a better way. In recent years, quick developments in the field
of privacy preserving monitors because of the progress in the skill to store data. Privacy preserving has become increasingly more general
because it allows sharing of privacy sensitive data for investigation purpose. People today have become aware of the privacy conflicts of their
sensitive data and are very doubtful to share data. Current techniques are different among each other with respect to number of conditions
such as, data quality, privacy level and performance. These techniques face different types of difficulties like, homogeneity and background
knowledge concerns. The main problem is the misuse of data and cause of data misuse is mining procedure. In this research work a hybrid
methodology of k-anonymity and randomization was used for different privacy protective difficulties. The recommended approach secures
individual information with no loss of data which makes ease of use of information.