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Privacy-preserving data mining aims at securely extracting knowledge from two or more parties' private data. Secure multi-party computation is the paramount approach to it. In this paper, we study privacy-preserving add and multiply exchanging technology and present three new different approaches to privacy-preserving add to multiply protocol. After that, we analyze and compare the three different approaches about the communication overheads, the computation efforts and the security. In addition, we extend privacy-preserving add to multiply protocol to privacy-preserving adding to scalar product protocol, which is more secure and more useful in the high security situations of privacy-preserving data mining. Meantime, we present a solution for the new protocol.