Implementation of Multi-Level Trust in Privacy Preserving Data Mining against Non-Linear Attack

-The study of perturbation based PPDM approaches introduces random perturbation that is number of changes made in the original data. The limitation of previous solution is single level trust on data miners but new work is perturbation based PPDM to multilevel trust. When data owner sends number of pertubated copy to the trusted third party that time adversary cannot find the original copy from the pertubated copy means the adversary diverse from original Copy this is known as the diversity attack. To prevent diversity attack is main goal of MLT-PPDM services. The malicious data miner has different pertubated copy by applying different MLT-PPDM algorithms to add the noise into original data. By applying LLSEE and Nonlinear error estimation algorithm to calculate how much noise present into original data, do prediction that how much get original data very accurately from this diverse copy. The comparative study between LLSEE and Nonlinear error estimation to decide that nonlinear error estimation gives maximum accuracy. The previous work is limitated only for linear attack means linear function. But proposed result is work on the non-linear attack also means nonlinear function estimation. Keywords— Diversity Attack, K-Anonymity, Multi-Level Trust, Non-Linear error estimation, Parallel Generation. Sequence Generation, On Demand Generation, LLSEE. __________________________________________________*****_________________________________________________