Integration of Partial Information Hiding Technology with Multi level Trust Privacy Preserving Data Mining
Privacy Preserving Data Mining (PPDM) addresses the problem of developing accurate models about aggregated data without access to precise information in individual data record. A widely studied perturbation based PPDM approach introduces additive perturbation. Previous solutions of this approach are limited in their tacit assumption of single level trust on data miners. In this work, the scope of perturbation based PPDM is expanded to Multilevel Trust (MLT PPDM) In this setting, the more trusted a data miner is, the less perturbed copy of the data it can access. Under this setting, a malicious data miner may have access to differently perturbed copies of the same data through various means, and may combine these diverse copies to jointly infer additional information about the original data that the data owner does not intend to release. Preventing such diversity attacks is the key challenge of providing MLT PPDM services. This challenge is addressed by properly correlating perturbation across copies at different trust levels. This solution allows a data owner to generate perturbed copies of its data for arbitrary trust levels on demand. This feature offers data owner"s maximum flexibility.