Privacy Preserving Data Mining (PPDM) is used to extract relevant knowledge from large amount of data and at the same time protect the sensitive information from the data miners. The problem in privacy-sensitive domain is solved by the development of the Multi-Level Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the same data is available to data miners at different trusted levels. In MLT-PPDM data owners generate perturbed data by various techniques like Parallel generation, Sequential generation and On-demand generation. MLT-PPDM is robust against the diversity attacks. In my work partial information hiding methodologies like random rotation perturbation, retention replacement omega replica and K-anonymity are incorporated with MLT-PPDM to enhance data security and to prevent leakage of the sensitive data. Finally MLT-PPDM approach is improved to tackle against the non-linear attacks.