Abstract: To achieve the goal that personal and original information is not disclosed to each other when several parties cooperatively mine several data tables at different computational sites, based on secure triple-party protocol, a triple-site cross multi-table frequent item sets mining algorithm with privacy preserving was proposed in distributed environment with multiple tables at each site. The proposed algorithm disturbed data by generating random numbers, mined frequent item sets of inter-site in parallel, and linked the data with equal-value by common link attribution of the tables among the sites and applied secure protocol to compute the global support of inter-site cross-table frequent item sets. The experimental results show that the proposed algorithm is efficient, and it can not only mine the cross multi-table frequent item sets, but also preserve the private data at each site.

Key words: cross multi-table mining, frequent item set, parallel mining, privacy preserving, secure multi-party protocol

摘要： 从多方合作挖掘分布存储在不同计算站点上多个数据库表而不泄露各方原始数据信息的目的出发，对于每个站点拥有多个数据表的分布式计算环境，基于三方安全协议，运用生成随机数扰乱方法，采取各站点并行挖掘频繁项集,将站点间各表数据公共连接属性作等值连接,以安全协议计算全局站间跨表频繁项集支持数的策略，提出了一站多表的3站点跨多表频繁项集挖掘隐私保护算法。实验结果表明，该算法在高效地联合挖掘出跨多表频繁项集的同时保护了各站点的敏感信息。

关键词: 跨表挖掘, 频繁项集, 并行挖掘, 隐私保护, 多方安全协议