Privacy Preserving Data Mining (PPDM) is commonly utilized for the purpose of extracting related knowledge from large amount of data and simultaneously safeguard the sensitive information from the data miners. The major complication in privacy-sensitive domain is solved through the development of the Multi-Level Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the single data is available to data miners at different trusted levels. In this scheme, data owners produce perturbed data through various schemes like Parallel generation, Sequential generation and On-demand generation. MLT-PPDM is extremely robust against the diversity attacks. In this work partial information hiding schemes like random rotation perturbation, retention replacement and K-anonymity are incorporated together with MLT-PPDM for the purpose of enhancing the data security and to prevent leakage of the sensitive data. At last, MLT-PPDM approach is improved in order to tackle against the non-linear attacks.