分布式环境下保持隐私的关联规则挖掘  

Privacy preserving distributed data mining association rules of frequent itemsets

作　　者：张宏壮[1] 王建民[1] 胡畅霞[1] 

机构地区：[1]石家庄铁道学院信息工程系,河北石家庄050043

出　　处：《河北省科学院学报》2007年第3期20-23,共4页Journal of The Hebei Academy of Sciences

摘　　要：保持隐私是未来数据挖掘领域的焦点问题之一,如何在不共享精确数据的条件下,获取准确的数据关系是保持隐私的数据挖掘的首要任务。本文利用向量点积方法从垂直型分布数据中挖掘关联规则,并且保持其隐私性。给出了数量积算法和隐私挖掘的步骤,最后举例说明了如何利用数量积算法进行垂直型分布式数据挖掘。There has been growing interests in private concerns for future data mining research. Privacy preserving data mining concentrates on developing accurate models without sharing precise individual data records. A privacy preserving association rule mining algorithm was introduced. This algorithm preserved privacy of individual values by computing scalar product, meanwhile the algorithm of computing scalar product was given and the security was analyzed.

关 键 词：保持隐私 分布式数据挖掘 关联规则 频繁项集 点积 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]