Perturbation based preserving data mining explains the problem of developing accurate models about aggregated data without access to precise information in individual data record. A advanced perturbation-based preserving data mining approach introduces random perturbation to individual values to preserve privacy before data are published. Various previous solutions for this approach are limited in their tacit assumption of constant multi-levels trust on data miners. In this paper, the authors relax this assumption and expand the scope of perturbation-based to on demand multilevel trust on data. In their setting, the more trusted a data miner is the less perturbed copy of the data it can access.