Due to the various advantages of cloud computing, users tend to outsource data mining task to professional cloud service providers. However, user's privacy cannot be guaranteed. Currently, while many scholars are concerned about how to protect sensitive data from unauthorized access, few scholars engage research on data analysis. But if potential knowledge cannot be mined, the value of big data may not be fully utilized. This paper proposes a privacy preserving data mining (PPDM) method based on lattice, which support ciphertext intermediate point and distance homomorphic computing. Meanwhile, it builds a privacy preserving cloud ciphertext data clustering data mining Method. Users encrypt privacy data before outsource the data to cloud service providers, cloud service providers use homomorphic encryption to achieve privacy protection mining algorithms including k-means, hierarchical clustering and DBSCAN. Compared with the existing PPDM method, the presented method with high security is based on shortest vector difficulties (SVP) and the closest vector problem (CVP). Meanwhile, it maintains the accuracy of distance between two data, providing mining results with high accuracy and availability. Experiments are designed for the privacy preserving cluster mining (PPCM) with cardiac arrhythmia datasets of machine learning, and the experimental results show that the method based on lattice ensure not only security but also accuracy and performance.