Privacy preserving data mining: A noise addition framework using a novel clustering technique

Abstract

<p>During the whole process of data mining (from data collection to knowledge discovery) various sensitive data get exposed to several parties including data collectors, cleaners, preprocessors, miners and decision-makers. The exposure of sensitive data can potentially lead to breach of individual privacy. Therefore, many privacy preserving techniques have been proposed recently. In this paper we present a framework that uses a few novel noise addition techniques for protecting individual privacy while maintaining a high data quality. We add noise to all attributes, both numerical and categorical. We present a novel technique for clustering categorical values and use it for noise addition purpose. A security analysis is also presented for measuring the security level of a data set.</p>