An Adaptive Privacy Preserving Data Mining Model under Distributed Environment

Abstract

Privacy preserving becomes an important issue in the development progress of data mining techniques, especially in distributed data mining. Secure multiparty computation methods are proposed to protect the privacy in distributed environment, but shows low performance under massive nodes. This paper presents an adaptive privacy preserving data mining model based on data perturbation method to improve the efficiency while preserving the privacy. Security capability of basic data perturbation is firstly analyzed and an adaptive enhancement method is proposed according to the eigen value decomposition based attacks. A light-weight protocol with homomorphic technique is proposed to perform the perturbation process under distributed environments. The experiment results show that the model has high controllable security and shows more efficiency in large scale distribution environment comparing to secure multiparty related methods.