Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/639202
Title: Achieve personalized anonymity through query blocks exchanging
Authors: Chunguang Ma;Lei Zhang;Songtao Yang;Xiaodong Zheng;Pinhui Ke
subject: query and location privacy|location-based services|cyberspace security|collaborative users|personalized anonymity
Year: 2016
Publisher: IEEE
Abstract: In cyberspace security, the privacy in location-based services (LBSs) becomes more critical. In previous solutions, a trusted third party (TTP) was usually employed to provide disturbance or obfuscation, but it may become the single point of failure or service bottleneck. In order to cope with this drawback, we focus on another important class, establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms, we found users in the group must share the same maximum anonymity degree, and they could not ease the process of preservation in a lower one. To cope with this problem, we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms, and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally, experimental evaluations further verify its effectiveness and efficiency.
Description: 
URI: http://localhost/handle/Hannan/174162
http://localhost/handle/Hannan/639202
ISSN: 1673-5447
volume: 13
issue: 11
Appears in Collections:2016

Files in This Item:
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Title: Achieve personalized anonymity through query blocks exchanging
Authors: Chunguang Ma;Lei Zhang;Songtao Yang;Xiaodong Zheng;Pinhui Ke
subject: query and location privacy|location-based services|cyberspace security|collaborative users|personalized anonymity
Year: 2016
Publisher: IEEE
Abstract: In cyberspace security, the privacy in location-based services (LBSs) becomes more critical. In previous solutions, a trusted third party (TTP) was usually employed to provide disturbance or obfuscation, but it may become the single point of failure or service bottleneck. In order to cope with this drawback, we focus on another important class, establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms, we found users in the group must share the same maximum anonymity degree, and they could not ease the process of preservation in a lower one. To cope with this problem, we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms, and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally, experimental evaluations further verify its effectiveness and efficiency.
Description: 
URI: http://localhost/handle/Hannan/174162
http://localhost/handle/Hannan/639202
ISSN: 1673-5447
volume: 13
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7781722.pdf1.31 MBAdobe PDFThumbnail
Preview File
Title: Achieve personalized anonymity through query blocks exchanging
Authors: Chunguang Ma;Lei Zhang;Songtao Yang;Xiaodong Zheng;Pinhui Ke
subject: query and location privacy|location-based services|cyberspace security|collaborative users|personalized anonymity
Year: 2016
Publisher: IEEE
Abstract: In cyberspace security, the privacy in location-based services (LBSs) becomes more critical. In previous solutions, a trusted third party (TTP) was usually employed to provide disturbance or obfuscation, but it may become the single point of failure or service bottleneck. In order to cope with this drawback, we focus on another important class, establishing anonymous group through short-range communication to achieve k-anonymity with collaborative users. Along with the analysis of existing algorithms, we found users in the group must share the same maximum anonymity degree, and they could not ease the process of preservation in a lower one. To cope with this problem, we proposed a random-QBE algorithm to put up with personalized anonymity in user collaboration algorithms, and this algorithm could preserve both query privacy and location privacy. Then we studied the attacks from passive and active adversaries and used entropy to measure user's privacy level. Finally, experimental evaluations further verify its effectiveness and efficiency.
Description: 
URI: http://localhost/handle/Hannan/174162
http://localhost/handle/Hannan/639202
ISSN: 1673-5447
volume: 13
issue: 11
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7781722.pdf1.31 MBAdobe PDFThumbnail
Preview File