中文摘要：

由于云计算的诸多优势，用户倾向于将数据挖掘和数据分析等业务外包到专业的云服务提供商，然而随之而来的是用户的隐私不能得到保证．目前，众多学者关注云环境下敏感数据存储的隐私保护问题，而隐私保护数据分析的相关研究还比较少．但是如果仅仅为了保护数据隐私，而不对大数据进行挖掘分析，大数据也就失去了其潜在的巨大价值．提出了一种云计算环境下基于格的隐私保护数据挖掘方法，利用格加密构建隐私数据的安全同态运算方法，并且在此基础上实现了支持隐私保护的云端密文数据聚类分析数据挖掘服务．为保护用户数据隐私月户将数据加密之后发布给云服务提供商，云服务提供商利用基于格的同态加密算法实现隐私保护的k-means、隐私保护层次聚类以及隐私保护DBSCAN数据挖掘服务，但云服务提供商并不能直接访问用户数据破坏用户隐私与现有的隐私数据发布方法相比，隐私数据发布基于格的最接近向量困难问题（CVP）和最短向量困难问题（SVP）具有很高的安全性．同时，有效保持了密文数据间距离的精确性.与现有研究相比，挖掘结果也具有更高的精确性和可用性．对方法的安全性进行了理论分析，并设计实验对提出的隐私保护数据挖掘方法效率进行评估，实验结果表明，提出的基于格的隐私保护数据挖掘算法与现有的方法相比具有更高的数据分析精确性和计算效率．

英文摘要：

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.