Page 215 - 《软件学报》2021年第11期

软件学报 ISSN 1000-9825, CODEN RUXUEW E-mail: jos@iscas.ac.cn
Journal of Software,2021,32(11):3541−3562 [doi: 10.13328/j.cnki.jos.006044] 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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面向频繁项集挖掘的本地差分隐私事务数据收集方法

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欧阳佳 , 印 鉴 , 肖政宏 , 赵慧民 , 刘少鹏 , 梁 鹏 , 肖茵茵 1
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(广东技术师范大学 计算机科学学院,广东 广州 510665)
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(中山大学 人工智能学院,广东 广州 510006)
通讯作者: 肖政宏, E-mail: huasxzh@126.com

摘 要: 事务数据常见于各种应用场景中,如购物记录、页面浏览历史等.为了提供更好的服务,服务提供商收集
用户数据并进行分析,但收集事务数据会泄露用户的隐私信息.为了解决上述问题,基于压缩的本地差分隐私模型,
提出一种事务数据收集方法.首先,定义了一种新的候选项集分值函数;其次,基于该函数,将候选项集的样本空间划
分为多个子空间;然后,随机选择其中一个子空间,基于该子空间随机生成事务数据并发送给不可信的数据收集者;
最后,考虑到隐私参数的设置问题,基于最大后验置信度攻击模型设计启发式隐私参数设置策略.理论分析表明,该
方法能够同时保护事务数据的长度与内容,满足压缩的本地差分隐私要求.实验结果表明,与目前最优的工作相比,
所收集的数据具有更高的效用性,隐私参数设置更具有语义性.
关键词: 隐私保护;数据收集;事务数据;本地差分隐私;隐私参数
中图法分类号: TP309

中文引用格式: 欧阳佳,印鉴,肖政宏,赵慧民,刘少鹏,梁鹏,肖茵茵.面向频繁项集挖掘的本地差分隐私事务数据收集方法.软
件学报,2021,32(11):3541−3562. http://www.jos.org.cn/1000-9825/6044.htm
英文引用格式: Ouyang J, Yin J, Xiao ZH, Zhao HM, Liu SP, Liang P, Xiao YY. Transaction data collection for itemset mining
under local differential privacy. Ruan Jian Xue Bao/Journal of Software, 2021,32(11):3541−3562 (in Chinese). http://www.jos.
org.cn/1000-9825/6044.htm

Transaction Data Collection for Itemset Mining Under Local Differential Privacy

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OUYANG Jia , YIN Jian , XIAO Zheng-Hong , ZHAO Hui-Min , LIU Shao-Peng , LIANG Peng ,
XIAO Yin-Yin 1
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(School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China)
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(School of Artificial Intelligence, Sun Yat-Sen University, Guangzhou 510006, China)
Abstract: Transaction data is commonly in various application scenarios, such as shopping records, page browsing history, etc., service
providers collect and analyze transaction data for providing better services. However, collecting transaction data will disclose privacy
information. To solve the problem, this study proposes a transaction data collection mechanism based on condensed local differential
privacy (CLDP). Firstly, a new score function of the candidate set is defined. Secondly, the output domain of the candidate set is separated
into several subspaces according to the function. Thirdly, the client selects one subspace randomly, and generates transaction data
randomly based on the subspace, then, sends it to the untrusted data collector. Finally, considering the difficulty for setting the privacy
parameter, the heuristic privacy parameter setting strategy is designed based on the maximum posterior confidence threat model (MPC).
The theoretical analysis shows that this method can protect the length and content of transaction data at the same time and satisfies

∗ 基金项目: 国家自然科学基金(61702119, U1711262, U1501252, U1711261); 广州市科技计划(201804010236, 201607010152);
广东省基础与应用基础研究基金(2019A1515012048); 广东省教育厅创新团队项目(2017KCXTD021)
Foundation item: National Natural Science Foundation of China (61702119, U1711262, U1501252, U1711261); Science and
Technology Program of Guangzhou Municipality (201804010236, 201607010152); Basic and Applied Basic Research Foundation of
Guangdong Province (2019A1515012048); Innovation Team Project of Education Department of Guangdong Province (2017KCXTD021)
收稿时间: 2019-11-06; 修改时间: 2020-01-30, 2020-03-09; 采用时间: 2020-03-20; jos 在线出版时间: 2021-05-20