Data mining is a process to extract useful knowledge from huge amounts of data. To conduct data mining, we often need to collect data from different parties. Due to privacy constriction, accessing all the data may not be allowed. How can multiple parties collaboratively conduct data mining without breaching data privacy presents a grand challenge. Theoretical results from the area of secure multi-party computation show that assuming the existence of trapdoor permutations, one may provide secure protocols for any multi-party computation with honest majority. However, the general methods are far from efficiency and practicality for computing complex functions on inputs consisting of large sets of data. Therefore, to efficiently tackle the problem that is formulated as Privacy Preserving Collaborative Data Mining, secure solutions with decent efficiency are demanding. In this talk, I will present practical solutions to the problem.