A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services

2020 ◽  
Vol 527 ◽  
pp. 406-419 ◽  
Author(s):  
Shaobo Zhang ◽  
Xinjun Mao ◽  
Kim-Kwang Raymond Choo ◽  
Tao Peng ◽  
Guojun Wang
Keyword(s):  
Privacy Preserving ◽  
Location Based Services ◽  
Continuous Location

scholarly journals GLPP: A Game-Based Location Privacy-Preserving Framework in Account Linked Mixed Location-Based Services

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Zhuo Ma ◽  
Jiuxin Cao ◽  
Xiusheng Chen ◽  
Shuai Xu ◽  
Bo Liu ◽  
...  
Keyword(s):  
Location Privacy ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Behavioral Analysis ◽  
Continuous Location ◽  
Real Dataset ◽  
Privacy Leakage ◽  
Cross Platform ◽  
Inference Attacks ◽  
Zero Sum

In Location-Based Services (LBSs) platforms, such as Foursquare and Swarm, the submitted position for a share or search leads to the exposure of users’ activities. Additionally, the cross-platform account linkage could aggravate this exposure, as the fusion of users’ information can enhance inference attacks on users’ next submitted location. Hence, in this paper, we propose GLPP, a personalized and continuous location privacy-preserving framework in account linked platforms with different LBSs (i.e., search-based LBSs and share-based LBSs). The key point of GLPP is to obfuscate every location submitted in search-based LBSs so as to defend dynamic inference attacks. Specifically, first, possible inference attacks are listed through user behavioral analysis. Second, for each specific attack, an obfuscation model is proposed to minimize location privacy leakage under a given location distortion, which ensures submitted locations’ utility for search-based LBSs. Third, for dynamic attacks, a framework based on zero-sum game is adopted to joint specific obfuscation above and minimize the location privacy leakage to a balanced point. Experiments on real dataset prove the effectiveness of our proposed attacks in Accuracy, Certainty, and Correctness and, meanwhile, also show the performance of our preserving solution in defense of attacks and guarantee of location utility.

A Dual Privacy Preserving Scheme in Continuous Location-Based Services

2018 ◽  
Vol 5 (5) ◽  
pp. 4191-4200 ◽  
Author(s):  
Shaobo Zhang ◽  
Guojun Wang ◽  
Md Zakirul Alam Bhuiyan ◽  
Qin Liu
Keyword(s):  
Privacy Preserving ◽  
Location Based Services ◽  
Continuous Location

ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

2021 ◽  
pp. 1-12
Author(s):  
Gokay Saldamli ◽  
Richard Chow ◽  
Hongxia Jin
Keyword(s):  
Service Providers ◽  
Smart Cities ◽  
User Interaction ◽  
Privacy Preserving ◽  
Location Based Services ◽  
Third Party ◽  
Location Information ◽  
Fine Grained ◽  
Location Sharing ◽  
Social Network Structure

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.

DistPreserv: Maintaining User Distribution for Privacy-Preserving Location-Based Services

2022 ◽  
pp. 1-1
Author(s):  
Yanbing Ren ◽  
Xinghua Li ◽  
Yinbin Miao ◽  
Robert Deng ◽  
Jian Weng ◽  
...  
Keyword(s):  
Privacy Preserving ◽  
Location Based Services
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