增量式隐私保护频繁模式挖掘算法  

Incremental frequent pattern mining algorithm for privacy-preserving

作　　者：张亚玲[1] 王婷 王尚平[1] 

机构地区：[1]西安理工大学计算机科学与工程学院,西安710048

出　　处：《计算机应用》2018年第1期176-181,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61572019);陕西省科学研究计划重点项目(2016JZ001);陕西省教育厅重点实验室科研计划项目(16JS078)~~

摘　　要：针对多数隐私保护的频繁模式挖掘算法需要多次数据库扫描以及计数时需要进行多次比较的不足,提出了一种增量的基于位图的部分隐藏随机化回答(IBRRPH)算法。首先,引入bitmap表示数据库中的事务,采用"位与"操作有效提高支持度的计算速度;其次,通过分析增量访问关系,引入增量更新模型,使得在数据增量更新时频繁模式挖掘最大限度地利用了之前挖掘结果。针对增量分别为1 000至40 000,与顾铖等提出的算法(顾铖,朱保平,张金康.一种改进的隐私保护关联规则挖掘算法.南京航空航天大学学报,2015,47(1):119-124)进行了对比测试实验。实验结果表明,与顾铖等提出的算法相比,IBRRPH算法的效率提高幅度超过21%。Aiming at the problems that a database is scanned for multiple times and a record is compared for many times to count in most frequent pattern mining algorithms for privacy-preserving, an Incremental Bitmap-based Randomized Response with Partial Hiding （IBRRPH） algorithm was proposed. Firstly, the bitmap technique was used to represent the transaction in the database, and the ＂and＂ operator for bit was used to speed up the support degree calculating. Secondly, an incremental update model was introduced by analyzing incremental access relationship, so that the mining result before was used to the maximum limit during incremental updating. The contrast experiment of performance to the algorithm proposed by Gu et al. （GU C, ZHU B P, ZHANG J K. Improved algorithm of privacy preserving association rule mining. Journal of Nanjing University of Aeronautics ＆ Astronautics, 2015, 47（ 1）： 119 -124） was done aiming at the increment range from 1 000 to 40000. The experimental results show that the efficiency of the IBRRPH algorithm is improved over 21% compared to the algorithm proposed by Gu et al.

关 键 词：频繁模式挖掘 隐私保护 增量更新 部分隐藏的随机化回答算法 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构] TP393.08[自动化与计算机技术—计算机科学与技术]