A Voronoi-Based Location Privacy-Preserving Method for Continuous Query in LBS

Abstract

Most of the mobile phones have GPS sensors which make location based service (LBS) applicable. LBS brings not only convenience but also location privacy leak to us. Achieving anonymity and sending private queries are two main privacy-preserving courses in LBS. A novel location privacy-preserving method is proposed based on Voronoi graph partition on road networks. Firstly, based on the prediction of a user's moving direction, a cooperative -anonymity method is proposed without constructing cloaking regions which may lead to efficiency decline in continuous query. And then, a query algorithm is proposed without providing any user's actual location, replaced by continuous anchor sequence, to LBS provider. This algorithm can work out precise results according to candidate sets returned by LBS provider and it also solves uneven distribution problem in SpaceTwist. Performance analysis and experiments show that our method achieves a preferable tradeoff between QoS and location privacy preserving; it has obvious advantages compared with other methods.

One More Accuracy k-Anonymity Framework for LBS

“…But in all these approaches exist a problem that is the route from the start position that the request is originated to the center of the cloaked area may be more complicated than a straight line, which is more likely nonlinear. However, in the existing approaches, AS computes the route according to the Euclidean distance formula, which will result in the [15][16][17] focus on privacy protection in the road network. Ma et al [15] proposed to divide the road network according to Taylor polygon and use SpaceTwist technology to protect the privacy in continuous queries.…”

“…However, in the existing approaches, AS computes the route according to the Euclidean distance formula, which will result in the [15][16][17] focus on privacy protection in the road network. Ma et al [15] proposed to divide the road network according to Taylor polygon and use SpaceTwist technology to protect the privacy in continuous queries. Sun et al [16] proposed a location-label-based approach to meet l-diversity requirement, in which the road network is introduced on AS and participated into many cells.…”

Users Collaborative Mix-Zone to Resist the Query Content and Time Interval Correlation Attacks

“…This type of fashion makes privacy preservation of user collaboration no longer efficient, because they often submit queries with certain time delays. Thus, researchers have to go back to TTP for finding solutions and solutions of [1,3,4,7,9,10,26,27] are able to be classified into two categories: whole trajectory disturbance and subsequent locations generalization. As the adversary can obtain user's subtrajectory, algorithms of the whole trajectory disturbance are less suitable for continuous LBSs, so algorithms of subsequent locations generalization prosperous.…”

“…These algorithms can be classified into two main categories: the whole trajectory disturbance [1][2][3][4] and the subsequent location generalization [5][6][7][8][9][10]. As subtrajectories can be obtained before preserving, the adversary may infer the location correlation and identify the real trajectory from the anonymous group, which makes algorithms of whole trajectory disturbance failed.…”

“…In order to generalize subsequent locations, different algorithms based on obfuscation as well as disturbance are provided. In types of obfuscation, Ma et al [9] obfuscated the correlation with anchors, and cloaked the query content and time interval with users around the anchor's vicinity. Schlegel et al [10] utilized a user-define privacy grid, and obfuscated the requested point of interests (PoIs) with randomly chosen cells.…”