典型文献
IDEA:A Utility-Enhanced Approach to Incomplete Data Stream Anonymization
文献摘要：
The prevalence of missing values in the data streams collected in real environments makes them impossible to ignore in the privacy preservation of data streams.However,the development of most privacy preservation methods does not consider missing values.A few researches allow them to participate in data anonymization but introduce extra considerable information loss.To balance the utility and privacy preservation of incomplete data streams,we present a utility-enhanced approach for Incomplete Data strEam Anonymization (IDEA).In this approach,a slide-window-based processing framework is introduced to anonymize data streams continuously,in which each tuple can be output with clustering or anonymized clusters.We consider the dimensions of attribute and tuple as the similarity measurement,which enables the clustering between incomplete records and complete records and generates the cluster with minimal information loss.To avoid the missing value pollution,we propose a generalization method that is based on maybe match for generalizing incomplete data.The experiments conducted on real datasets show that the proposed approach can efficiently anonymize incomplete data streams while effectively preserving utility.
文献关键词：
中图分类号：
作者姓名：
Lu Yang;Xingshu Chen;Yonggang Luo;Xiao Lan;Wei Wang
作者机构：
College of Computer Science,Sichuan University,Chengdu 610065,China;School of Cyber Science and Engineering,Sichuan University,Chengdu 610065,China;Cyber Science Research Institute,Sichuan University,Chengdu 610065,China
文献出处：
引用格式：
[1]Lu Yang;Xingshu Chen;Yonggang Luo;Xiao Lan;Wei Wang-.IDEA:A Utility-Enhanced Approach to Incomplete Data Stream Anonymization)[J].清华大学学报自然科学版（英文版）,2022(01):127-140
A类：
Anonymization,strEam,anonymize
B类：
IDEA,Utility,Enhanced,Approach,Incomplete,Data,Stream,prevalence,missing,values,streams,collected,real,environments,makes,them,impossible,ignore,privacy,preservation,However,development,most,methods,does,not,few,researches,allow,participate,anonymization,extra,considerable,information,loss,To,balance,utility,incomplete,present,enhanced,approach,this,slide,window,processing,framework,introduced,continuously,which,each,tuple,can,output,clustering,anonymized,clusters,We,dimensions,attribute,similarity,measurement,enables,between,records,generates,minimal,avoid,pollution,generalization,that,maybe,match,generalizing,experiments,conducted,datasets,show,proposed,efficiently,while,effectively,preserving
AB值：
0.51601
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