The progress of location-based services has led to serious concerns on location privacy leakage. For effective and efficient location privacy preservation (LPP), existing methods are still not fully competent. They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them through synthesizing (AIs). The AIs refer to the synthesized traces which have similar semantic features to the actual traces, and do contain any target location. Two dedicated techniques are devised: the and . They play significant roles in two critical steps of synthesizing AIs. We conduct experiments on real datasets in two cities (Shanghai, China and Asturias, Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of @math and @math , and its run time of building the generators is only @math and @math seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations.
There has already existed plenty of remarkable studies on location privacy preservation @cite_25 @cite_17 @cite_3 @cite_28 @cite_4 @cite_33 . According to the application scenarios, the privacy preservation methods can be widely classified into three types @cite_9 .
@cite_4 @cite_33 @cite_28 @cite_9 @cite_3 @cite_25 @cite_17
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Privacy preservation has recently received considerable attention for location-based mobile services. Various location cloaking approaches have been proposed to protect the location privacy of mobile users. However, existing cloaking approaches are ill-suited for continuous queries. In view of the privacy disclosure and poor QoS (Quality of Service) under continuous query anonymization, in this paper, we propose a Δp-privacy model and a Δq-distortion model to balance the tradeoff between user privacy and QoS. Furthermore, two incremental utility-based cloaking algorithms --- bottom-up cloaking and hybrid cloaking, are proposed to anonymize continuous queries. Experimental results validate the efficiency and effectiveness of the proposed algorithms. In this paper, we address the topic of location privacy preservation of mobile users on road networks. Most existing techniques of privacy preservation rely on structure-based spatial cloaking, but pay little attention to location semantic information. Yet, location semantic information may disclose sensitive information about mobile users. Thus, we propose CloSed, a semantic-awareness privacy preservation model to protect users' privacy from violation. We design cloaked sets that should cover different semantic regions of road networks as well as satisfy quality of service QoS. As the problem of calculating the optimal cloaked set is NP-hard, we design a greedy algorithm that balances QoS and privacy requirements. Extensive experiments evaluations demonstrate the efficiency and effectiveness of our proposed algorithm in providing privacy guarantees on large real-world datasets. Sharing of location data enables numerous exciting applications, such as location-based queries, location-based social recommendations, monitoring of traffic and air pollution levels, etc. Disclosing exact user locations raises serious privacy concerns, as locations may give away sensitive information about individuals' health status, alternative lifestyles, political and religious affiliations, etc. Preserving location privacy is an essential requirement towards the successful deployment of location-based applications. These lecture notes provide an overview of the state-of-the-art in location privacy protection. A diverse body of solutions is reviewed, including methods that use location generalization, cryptographic techniques or differential privacy. The most prominent results are discussed, and promising directions for future work are identified. Table of Contents: Introduction Privacy-Preserving Spatial Transformations Cryptographic Approaches Hybrid Approaches Private Matching of Spatial Datasets Trajectory Anonymization Differentially Private Publication of Spatial Datasets Conclusions Mobile devices with positioning capabilities allow users to participate in novel and exciting location-based applications. For instance, users may track the whereabouts of their acquaintances in location-aware social networking applications, e.g., GoogleLatitude. Furthermore, users can request information about landmarks in their proximity. Such scenarios require users to report their coordinates to other parties, which may not be fully trusted. Reporting precise locations may result in serious privacy violations, such as disclosure of lifestyle details, sexual orientation, etc. A typical approach to preserve location privacy is to generate a cloaking region (CR) that encloses the user position. However, if locations are continuously reported, an attacker can correlate CRs from multiple timestamps to accurately pinpoint the user position within a CR. In this work, we protect against linkage attacks that infer exact locations based on prior knowledge about maximum user velocity. Assume user u who reports two consecutive cloaked regions A and B. We consider two distinct protection scenarios: in the first case, the attacker does not have information about the sensitive locations on the map, and the objective is to ensure that u can reach some point in B from any point in A. In the second case, the attacker knows the placement of sensitive locations, and the objective is to ensure that u can reach any point in B from any point in A. We propose spatial and temporal cloaking transformations to preserve user privacy, and we show experimentally that privacy can be achieved without significant quality of service deterioration.