文献类型：期刊文献

中文题名：基于序列格的隐私时序模式挖掘方法

英文题名：Private Time Series Pattern Mining with Sequential Lattice

作者：彭慧丽[1,2];金凯忠[1];付聪聪[1];付楠[1];张啸剑[1]

第一作者：彭慧丽

通讯作者：Zhang, Xiao-Jian

机构：[1]河南财经政法大学计算机与信息工程学院,河南郑州450002;[2]河南广播电视大学信息工程学院,河南郑州450046

第一机构：河南财经政法大学计算机与信息工程学院

年份：2020

卷号：48

期号：1

起止页码：153-163

中文期刊名：电子学报

外文期刊名：Acta Electronica Sinica

收录：CSTPCD;;EI(收录号：20201008271009);Scopus(收录号：2-s2.0-85080998030);北大核心:【北大核心2017】；CSCD:【CSCD2019_2020】；

基金：国家自然科学基金(No.61502146,No.91646203,No.91746115,No.61772131,No.61702161);河南省自然科学基金(No.162300410006);河南省科技攻关项目(No.162102310411);河南省教育厅高等学校重点科研项目(No.16A520002);河南省高等学校青年骨干教师培养计划(No.2017GGJS084);河南财经政法大学青年拔尖人才资助计划

语种：中文

中文关键词：差分隐私;时间序列;全局敏感度;数据挖掘;数据截断;序列格

外文关键词：differential privacy;time series;global sensitivity;data mining;data truncate;sequential lattice

摘要：基于差分隐私的时间序列模式挖掘方法中,序列的最大长度以及添加拉普拉斯噪声的多少直接制约着挖掘结果的可用性.针对现有时间序列模式挖掘方法全局敏感度过高、挖掘结果可用性较低的不足问题,提出了一种基于序列格的差分隐私下时间序列模式挖掘方法PrivTSM(Differentially Private Time Series Pattern Mining).该方法首先利用最长路径的策略对原始数据库进行截断处理;在此基础上,采用表连接操作生成满足差分隐私的序列格;结合序列格结构本身的特性,合理分配隐私预算,提高输出模式的可用性.理论分析表明PrivTSM方法满足ε-差分隐私,基于真实数据库上实验结果表明,PrivTSM方法的准确率TPR(True Postive Rate)和平均相对误差ARE(Average Relative Error)明显优于N-gram和Prefix-Hybrid方法.
Many methods of differentailly private time series pattern mining have been proposed,while in those methods,the length of sequence pattern and Laplace noise directly constrain the utility of the mining results.To address the questions caused by the global query sensitivity and lower utility of the existing works,an efficient method,called PrivTSM(differentially Private Time Series Pattern Mining) is proposed,which is based on sequence lattice for mining time series pattern with differential privacy.This method relies on the longest path strategy to truncate the original database;based on the truncated database,this method uses the table join operation to construct a differentially private sequence lattice.Furthermore,this method uses the property of the sequence lattice structure itself to allocate privacy budget reasonably and boost the accuracy of the noisy counts.PrivTSM satisfies ε-differential privacy through theoretical analysis.The experimental results on real datasets show that the accuracy(TPR) and average relative error(ARE) of the PrivTSM are better than those of the N-gram and Prefix-Hybrid algorithms.