Using unknowns to prevent discovery of association rules

Data mining technology has given us new capabilities to identify correlatio ns in large data sets. This introduces risks when the data is to be made pu blic, but the correlations are private. We introduce a method for selective ly removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. The effica cy and complexity of this method are discussed. We also present an experime nt showing an example of this methodology.