Privacy-preserving Naive Bayesian classifier for continuous data and discrete data

Abstract

In data analysis, providing privacy to the customer data is an important issue. In this paper, a large number of customers are surveyed to learn the rules of classification on their data while preserving privacy of the customers. Randomization techniques were proposed to address this problem. These techniques provide more accuracy for less privacy of customers, conversely less accuracy for more privacy of the customers. In this paper, we propose a cryptographic approach with strong privacy and no loss of accuracy as a cost of privacy for continuous data. Our approach uses naive Bayes classification algorithm using frequency mining. The result shows the efficiency of our approach.