Abstract: Privacy preserving is an important direction for data mining research. This paper is concentrated on the issue of using k-means clustering algorithm to mining interesting accurate models without sharing precise individual data records,and proposes a method based on secure multi-party computation model. The method uses a secure_mean protocol to accomplish privacy-preserving k-means clustering that based on a semi-trusted third-party server. It efficiently hides attribute values,preserves privacy information and guarantees valid clustering results.

摘要： 隐私保护是数据挖掘中一个重要的研究方向。针对如何在不共享精确数据的条件下,应用k-平均聚类算法从数据中发现有意义知识的问题,提出了一种基于安全多方计算的算法。算法利用半可信第三方参与下的安全求平均值协议,实现了在分布式数据中进行k-平均聚类挖掘时隐私保护的要求。实验表明算法能很好的隐藏数据,保护隐私信息,且对聚类的结果没有影响。