Mining Association Rules under Privacy Constraints

Abstract

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may impel users to provide spurious information. In this chapter, we study whether users can be encouraged to provide correct information by ensuring that the mining process cannot, with any reasonable degree of certainty, violate their privacy. Our analysis is in the context of extracting association rules from large historical databases, a popular mining process that identifies interesting correlations between database attributes. We analyze the various schemes that have been proposed for this purpose with regard to a variety of parameters including the degree of trust, privacy metric, model accuracy and mining efficiency.