We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classi cation hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classi cation hierarchy. A brief evaluation is performed, and some initial conclusions are made.

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A b s t r a c tData mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. We introduce a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. The efficacy and complexity of this method are discussed. We also present an experiment showing an example of this methodology.

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