Research on the Personalized Privacy Preserving Distributed Data Mining

In this paper we studied privacy preserving distributed data mining.The existing methods focus on a universal approach that exerts preservation in the same degree for all persons, without catering for their concrete needs. In view of this we innovatively proposed a new framework combining the Secure Multiparty Computation(SMC) with K-anonymity technology, and achieved personalized privacy preserving distributed data mining based on decision tree classification algorithm. Compared with other algorithms our methord could make a good trade-off point between privacy and accuracy, with high efficiency and low-overhead of computing and communication.