Work

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

Year: 2018
Type: article
Abstract: 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.
Language: English
Open Access status: hybrid
Estimated APC paid: $2,670
OpenAlex ID: W2804192025
FWCI: 0.121
Cites: 16
Cited by: 3
Related to: 10