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

Abstract

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.