Third Party Privacy Preserving Protocol for Perturbation Based Classification of Vertically Fragmented Data Bases
This addresses privacy concerns in distributed data mining for scenarios involving vertically fragmented databases, though it is incremental as it combines existing models.
The paper tackles the problem of privacy-preserving classification on vertically fragmented databases by proposing a third-party protocol that uses data perturbation techniques, achieving fairly high accuracy compared to classification on original undisguised data.
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.