Protection of location privacy in continuous LBSs against adversaries with background information

Abstract

Privacy issues in continuous Location-Based Services (LBSs) have gained attractive attentions in literature over recent years. In this paper, we illustrate the limitations of existing work and define an entropy-based privacy metric to quantify the privacy degree based on a set of vital observations. To tackle the privacy issues, we propose an efficient privacy-preserving scheme, DUMMY-T, which aims to protect LBSs user's privacy against adversaries with background information. By our Dummy Locations Generating (DLG) algorithm, we first generate a set of realistic dummy locations for each snapshot with considering the minimum cloaking region and background information. Further, our proposed Dummy Paths Constructing (DPC) algorithm guarantees the location reachability by taking the maximum distance of the moving mobile users into consideration. Security analysis and empirical evaluation results further verify the effectiveness and efficiency of our DUMMY-T.