典型文献
Utility-Improved Key-Value Data Collection with Local Differential Privacy for Mobile Devices
文献摘要：
The structure of key-value data is a typical data structure generated by mobile devices. The collection and analysis of the data from mobile devices are critical for service providers to improve service quality. Nevertheless, collecting raw data, which may contain various per?sonal information, would lead to serious personal privacy leaks. Local differential privacy (LDP) has been proposed to protect privacy on the device side so that the server cannot obtain the raw data. However, existing mechanisms assume that all keys are equally sensitive, which can?not produce high-precision statistical results. A utility-improved data collection framework with LDP for key-value formed mobile data is pro?posed to solve this issue. More specifically, we divide the key-value data into sensitive and non-sensitive parts and only provide an LDP-equivalent privacy guarantee for sensitive keys and all values. We instantiate our framework by using a utility-improved key value-unary en?coding (UKV-UE) mechanism based on unary encoding, with which our framework can work effectively for a large key domain. We then vali?date our mechanism which provides better utility and is suitable for mobile devices by evaluating it in two real datasets. Finally, some pos?sible future research directions are envisioned.
文献关键词：
中图分类号：
作者姓名：
TONG Ze;DENG Bowen;ZHENG Lele;ZHANG Tao
作者机构：
School of Computer Science and Technology,Xidian University,Xi'an 710071,China
文献出处：
引用格式：
[1]TONG Ze;DENG Bowen;ZHENG Lele;ZHANG Tao-.Utility-Improved Key-Value Data Collection with Local Differential Privacy for Mobile Devices)[J].中兴通讯技术（英文版）,2022(04):15-21
A类：
instantiate,unary,UKV
B类：
Utility,Improved,Key,Value,Data,Collection,Local,Differential,Privacy,Mobile,Devices,structure,typical,generated,by,mobile,devices,collection,analysis,from,are,critical,service,providers,quality,Nevertheless,collecting,raw,which,may,contain,various,information,would,lead,serious,personal,privacy,leaks,differential,LDP,has,been,proposed,protect,side,that,server,cannot,obtain,However,existing,mechanisms,assume,keys,equally,sensitive,produce,high,precision,statistical,results,utility,improved,framework,formed,solve,this,issue,More,specifically,divide,into,parts,only,equivalent,guarantee,values,We,our,using,UE,encoding,effectively,large,domain,then,vali,date,provides,better,suitable,evaluating,two,real,datasets,Finally,some,sible,future,research,directions,envisioned
AB值：
0.523467
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