Using unknowns to prevent discovery of association rules
ACM Sigmod Record, 2001•dl.acm.org
Data 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.
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.
Data 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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