Privacy Preserving Classification with Emerging Patterns

In privacy preserving classification, when data is stored in a centralized database and distorted using a randomization-based technique, we have information loss and reduced accuracy of classification. This paper presents a new approach to privacy preserving classification for centralized data based on Emerging Patterns. The presented solution gives higher accuracy of classification than a decision tree proposed in the literature, especially for high privacy. Effectiveness of this solution has been tested on real data sets and presented in this paper.