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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.
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