Retention replacement in privacy preserving classification
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
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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.