PP-TAN: a Privacy Preserving Multi-party Tree Augmented Naive Bayes Classifier

The rapid growth of Information and Communication Technologies emerges deep concerns on how data mining techniques and intelligent systems parse, analyze and manage enormous amount of data. Due to sensitive information contained within, data can be exploited by potential aggressors. Previous research has shown the most accurate approach to acquire knowledge from data while simultaneously preserving privacy is the exploitation of cryptography. In this paper we introduce an extension of a privacy preserving data mining algorithm designed and developed for both horizontally and vertically partitioned databases. The proposed algorithm exploits the multi-candidate election schema and its capabilities to build a privacy preserving Tree Augmented Naive Bayesian classifier. Security analysis and experimental results ensure the preservation of private data throughout mining processes.