典型文献
Precise sensitivity recognizing,privacy preserving,knowledge graph-based method for trajectory data publication
文献摘要：
1 Introduction
The existing privacy protection methods of trajectory data publication have two problems.First,existing methods[1-3]cannot resist attribute link attacks effectively.In practical scenarios,it is necessary to integrate data from a variety of fields for data analysis,such as cross-domain recommenda-tions.We usually need to obtain the user attributes outside of the trajectory data as background knowledge.Second,existing methods ignore the problem of adding unnecessary noise.Those works[4,5]have proposed some definitions of sensitive with the location itself or the frequency of location access.In real life,different people have different sensitivity to the same position.The sensitive locations are generally related to the location labels and the attributes of the user.However,most of the existing methods treat equally without discrimination incurring unnecessary noise addition.And the severe informa-tion loss has an adverse effect on the results of data analysis.Thus,designing an adaptive anonymization proceduire is crucial to increase data utility whilst maintaining user privacy.However,this requires that we accurately identify sensitive locations for each user,sensitivity varies from place to place and from place to user,which complicates the process.There-fore,this paper proposes a method for precise identification and privacy protection of sensitive locations for trajectory data release(PSR&PPM KG).
文献关键词：
中图分类号：
作者姓名：
Xianxian LI;Bing CAI;Li-e WANG;Lei LEI
作者机构：
Guangxi Key Lab of Multi-source Information Mining and Security,Guangxi Normal University,Guilin 541004,China;College of Computer Science and Information Engineering,Guangxi Normal University,Guilin 541004,China;Nanning Tiancheng Zhiyuan Intellectual Property Service Co.Ltd.,Nanning 530000,China
文献出处：
引用格式：
[1]Xianxian LI;Bing CAI;Li-e WANG;Lei LEI-.Precise sensitivity recognizing,privacy preserving,knowledge graph-based method for trajectory data publication)[J].计算机科学前沿,2022(04):211-213
A类：
proceduire
B类：
Precise,sensitivity,recognizing,privacy,preserving,knowledge,graph,trajectory,data,publication,Introduction,
The,existing,protection,methods,have,two,problems,First,cannot,resist,link,attacks,effectively,practical,scenarios,integrate,from,variety,fields,analysis,such,cross,domain,recommenda,We,usually,need,obtain,user,attributes,outside,background,Second,ignore,adding,unnecessary,noise,Those,works,proposed,some,definitions,sensitive,itself,frequency,access,real,life,different,people,same,position,locations,are,generally,related,labels,However,most,treat,equally,without,discrimination,incurring,addition,And,severe,informa,loss,has,adverse,results,Thus,designing,adaptive,anonymization,crucial,increase,utility,whilst,maintaining,this,requires,that,accurately,identify,each,varies,place,which,complicates,process,There,fore,paper,proposes,precise,identification,release,PSR,PPM,KG
AB值：
0.583264
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