Page 265 - 《软件学报》2021年第12期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
Journal of Software,2021,32(12):3929−3944 [doi: 10.13328/j.cnki.jos.006101] http://www.jos.org.cn
©中国科学院软件研究所版权所有. Tel: +86-10-62562563

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分组随机化隐私保护频繁模式挖掘

2
1
郭宇红 , 童云海 , 苏燕青 1
1
(国际关系学院 网络空间安全学院,北京 100091)
2 (北京大学 智能科学系,北京 100871)
通讯作者: 郭宇红, E-mail: yhguo@uir.cn

摘 要: 已有的隐私保护频繁模式挖掘随机化方法不考虑隐私保护需求差异性,对所有个体运用统一的随机化参
数,实施同等的保护,无法满足个体对隐私的偏好.提出基于分组随机化的隐私保护频繁模式挖掘方法 (grouping-
based randomization for privacy preserving frequent pattern mining,简称 GR-PPFM).该方法根据不同个体的隐私保护
要求进行分组,为每一组数据设置不同的隐私保护级别和与之相适应的随机化参数.在合成数据和真实数据中的实
验结果表明:相对于统一单参数随机化 mask,分组多参数随机化 GR-PPFM 不仅能够满足不同群体多样化的隐私保
护需求,还能在整体隐私保护度相同情况下提高挖掘结果的准确性.
关键词: 分组;随机化;个性化;隐私保护;频繁模式挖掘
中图法分类号: TP309

中文引用格式: 郭宇红,童云海,苏燕青.分组随机化隐私保护频繁模式挖掘.软件学报,2021,32(12):3929−3944. http://www.
jos.org.cn/1000-9825/6101.htm
英文引用格式: Guo YH, Tong YH, Su YQ. Privacy preserving frequent pattern mining based on grouping randomization. Ruan
Jian Xue Bao/Journal of Software, 2021,32(12):3929−3944 (in Chinese). http://www.jos.org.cn/1000-9825/6101.htm
Privacy Preserving Frequent Pattern Mining Based on Grouping Randomization

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GUO Yu-Hong , TONG Yun-Hai , SU Yan-Qing 1
1 (School of Cyber Science and Engineering, University of International Relations, Beijing 100091, China)
2 (Department of Machine Intelligence, Peking University, Beijing 100871, China)
Abstract: Existing randomization methods of privacy preserving frequent pattern mining use a uniform randomization parameter for all
individuals, without considering the differences of privacy requirements. This equal protection cannot satisfy individual preferences for
privacy. This study proposes a method of privacy preserving frequent pattern mining based on grouping randomization (referred to as
GR-PPFM). In this method, individuals are grouped according to their different privacy protection requirements. Different group of data is
assigned to different privacy protection level and corresponding random parameter. The experimental results of both synthetic and real-
world data show that compared with the uniform single parameter randomization of mask, grouping randomization with multi parameters
of GR-PPFM can not only meet the needs of different groups of diverse privacy protection, but also improve the accuracy of mining
results with the same overall privacy protection.
Key words: grouping; randomization; personalization; privacy preserving; frequent pattern mining

频繁模式挖掘应用广泛,比如:医学研究人员希望通过分析医学普查数据,发现疾病间的关联,获取并发症
[1]
等病学知识 ——例如患糖尿病的人通常伴随着冠心病和高血压.然而在数据普查时,出于隐私的考虑,许多人

∗ 基金项目: 国家自然科学基金(60403041); 中央高校基本科研业务费专项资金(3262017T48, 3262018T02)
Foundation item: National Natural Science Foundation of China (60403041); Fundamental Research Funds for the Central
Universities (3262017T48, 3262018T02)
收稿时间: 2019-08-28; 修改时间: 2019-12-24, 2020-04-04; 采用时间: 2020-06-16