Maintaining Data Privacy in Association Rule Mining

Speaker: Minghua ZHANG

Abstract

Data mining services require accurate input data for their results to be meaningful, but privacy concerns may influence users to provide spurious information. The authors investigate here, with respect to mining association rules, 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. The authors present a scheme, based on probabilistic distortion of user data, that can simultaneously provide a high degree of privacy to the user and retain a high level of accuracy in the mining results. The performance of the scheme is validated against representative real and synthetic datasets.

Referred Papers