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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 method could make a good trade-off point between privacy and accuracy, with high efficiency and low-overhead of computing and communication.