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.
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Andruszkiewicz, P. (2012). Retention Replacement in Privacy Preserving Classification. In: Morzy, T., Härder, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2012. Lecture Notes in Computer Science, vol 7503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33074-2_2
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DOI: https://doi.org/10.1007/978-3-642-33074-2_2
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