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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.

Continuous query in location-based services may reveal the attribute information of the user obliviously, and an adversary may utilize the attribute as background knowledge to correlate the real locations and to generate location trajectory. Thus, the adversary can obtain the personal privacy of the user. In order to cope with this problem, several algorithms had been proposed. However, these algorithms were mainly designed for snapshot query and failed to provide privacy protection service for continuous query. As a matter of fact, continuous anonymous regions can also be used as the trajectory of regions and one can obtain the real location trajectory through calibration. In addition, other algorithms designed for continuous query may also utilize a longer running time to achieve the attribute anonymity and affect the balance of quality of service and personal privacy. Therefore, in order to cope with the above two problems, this paper provides a PSO anonymization, short for particle swarm optimization anonymization algorithm. This algorithm utilizes the particle swarm optimization clustering algorithm to accelerate the process of finding similar attributes in attribute generalization. Furthermore, this algorithm also utilizes the randomly chosen anonymous cells to further generalize the anonymous region, so that it can provide better privacy protection and better service quality. At last, this paper utilizes security analysis and experimental verification to further verify the effectiveness and efficiency of both the level of privacy protection and algorithm execution.

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A user's staying points in her trajectory have semantic association with privacy, such as she stays at a hospital. Staying at a sensitive place, a user may have privacy exposure risks when she gets location based service (LBS). Constructing cloaking regions and using fake locations are common methods. But if regions and fake positions are still in the sensitive area, it is vulnerable to lead location privacy exposure. We propose an anchor generating method based on sensitive places diversity. According to the visiting number and peak time of users, sensitive places are chosen to form a diversity zone, its centroid is taken as the anchor location which increases a user's location diversity. Based on the anchor, a query algorithm for places of interest (POIs) is proposed, and precise results can be deduced with the anchor instead of sending users' actual location to LBS server. The experiments show that our method achieves a tradeoff between QoS and privacy preserving, and it has a good working performance.