Retention replacement in privacy preserving classification

Authors:
Piotr Andruszkiewicz
Affiliations:
Institute of Computer Science, Warsaw University of Technology, Poland
Venue:
ADBIS'12 Proceedings of the 16th East European conference on Advances in Databases and Information Systems
Year:
2012

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Abstract

In privacy preserving classification based on randomisation, the additive and multiplicative perturbation methods were shown as preserving little privacy. Thus, we focus on the retention replacement randomisation-based method for classification over centralised data. We propose how to build privacy preserving classifiers over data distorted by means of the retention replacement randomisation-based method. We consider the eager and lazy classifiers based on emerging patterns and the decision tree. We have tested our proposal and show that the high accuracy results in classification can be obtained with the usage of the retention replacement method.