In this paper, we address this challenge in enabling MLT-PPDM services. In particular, we focus on the additive perturbation approach where random Gaussian noise is added to the original data with arbitrary distribution, and provide a systematic solution. Through a one-to-one mapping, our solution allows a data owner to generate distinctly perturbed copies of its data according to different trust levels. In MLT-PPDM, data miners may have access to multiple perturbed copies. By combining multiple perturbed copies, data miners may be able to perform diversity attacks to reconstruct the original data more accurately than what is allowed by the data owner.
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