›› 2016, Vol. 29 ›› Issue (5): 88-.

• 论文 • 上一篇    下一篇

用于短频繁项的隐私保护关联规则挖掘方法

张翠翠,阮树骅   

  1. (四川大学 计算机学院,四川 成都 610065)
  • 出版日期:2016-05-15 发布日期:2016-05-24
  • 作者简介:张翠翠(1990—),女,硕士研究生。研究方向:人工智能等。阮树骅(1966—),女,硕士,副教授。研究方向:数据库系统等。

A Privacy Preserving Association Rules Mining Method for Short Frequent Itemsets

ZHANG Cuicui,RUAN Shuhua   

  1. (College of Computer,Sichuan University,Chengdu 610065,China)
  • Online:2016-05-15 Published:2016-05-24

摘要:

随着数据量的增长,隐私保护的问题也愈发突出,文中是介绍了目前数据挖掘过程中隐私保护相关的基本技术,提出了一种数据集中式分布下布尔数据集的关联规则的挖掘算法,此方法在实现了隐私保护的同时,通过与或运算实现了数据集的压缩。相关实验数据表明,该算法有效减少了挖掘时间,并保证了误差在可接受的范围之内。

关键词: 隐私保护, 关联规则, 压缩, 数据挖掘, 短频繁项集

Abstract:

The explosion of data poses increasingly challenges on privacy preservation.This paper introduces basic technologies related to the privacy preservation in data mining,and puts forward a mining algorithm under association rule to deal with the Boolean data set distributed in a centralized manner.The method compresses the data set while preserving privacy,thus enormously reducing the time of data mining.Test results show that the mining time is significantly reduced with acceptable errors by this algorithm.

Key words: privacy preservation;association rule;compression;data mining;short frequent itemsets

中图分类号: 

  • TP311.563