  Privacy is become major issue in distributed data mining. In the literature
we can found many proposals of privacy preserving which can be divided into two
major categories that is trusted third party and multiparty based privacy
protocols. In case of trusted third party models the conventional asymmetric
cryptographic based techniques will be used and in case of multi party based
protocols data perturbed to make sure no other party to understand original
data. In order to enhance security features by combining strengths of both
models in this paper, we propose to use data perturbed techniques in third
party privacy preserving protocol to conduct the classification on vertically
fragmented data bases. Specially, we present a method to build Naive Bayes
classification from the disguised and decentralized databases. In order to
perform classification we propose third party protocol for secure computations.
We conduct experiments to compare the accuracy of our Naive Bayes with the one
built from the original undisguised data. Our results show that although the
data are disguised and decentralized, our method can still achieve fairly high
accuracy.
