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.
(1) Anonymizing the actual location in a set of locations of other users. This anonymization mechanism is a function to hide the user in the class with @math users in @cite_10 @cite_37 @cite_27 @cite_14 @cite_21 @cite_31 @cite_24 . @cite_31 , selected the fake traces among others with some strict constraints for a targeted real trace. Although this protection performs well, other's privacy was leaked during this process. @cite_14 divided the area into clusters (namely @math ) where each cluster includes @math users. When a client proposes a query to the LBS provider, the boundary of the area he belongs to is sent to the servers. However, this method limits an adversary's probability to infer the accurate location to @math . If there are no @math clients near the user, the @math will be too large to provide satisfactory service. Moreover, this exposes the general location of them. @cite_27 extracted the semantic features of regions by the distribution of nearby users' staying duration of locations. By the semantic features, they computed the cloaking area. However, the approach is invalid when there are not enough other users around.
@cite_37 @cite_14 @cite_21 @cite_24 @cite_27 @cite_31 @cite_10
1541376630 2157855380 2146905023 2004986927
With the wide adoption of handheld devices (e.g., smartphones, tablets), a large number of location-based services (also called LBSs) have flourished providing mobile users with real-time and contextual information on the move. Accounting for the amount of location information they are given by users, these services are able to track users wherever they go and to learn sensitive information about them (e.g., their points of interest including home, work, religious or political places regularly visited). A number of solutions have been proposed in the past few years to protect users location information while still allowing them to enjoy geo-located services. Among the most robust solutions are those that apply the popular notion of differential privacy to location privacy (e.g., Geo-Indistinguishability), promising strong theoretical privacy guarantees with a bounded accuracy loss. While these theoretical guarantees are attracting, it might be difficult for end users or practitioners to assess their effectiveness in the wild. In this paper, we carry on a practical study using real mobility traces coming from two different datasets, to assess the ability of Geo-Indistinguishability to protect users' points of interest (POIs). We show that a curious LBS collecting obfuscated location information sent by mobile users is still able to infer most of the users POIs with a reasonable both geographic and semantic precision. This precision depends on the degree of obfuscation applied by Geo-Indistinguishability. Nevertheless, the latter also has an impact on the overhead incurred on mobile devices resulting in a privacy versus overhead trade-off. Finally, we show in our study that POIs constitute a quasi-identifier for mobile users and that obfuscating them using Geo-Indistinguishability is not sufficient as an attacker is able to re-identify at least 63 of them despite a high degree of obfuscation. Mobile devices equipped with positioning capabilities (e.g., GPS) can ask location-dependent queries to Location Based Services (LBS). To protect privacy, the user location must not be disclosed. Existing solutions utilize a trusted anonymizer between the users and the LBS. This approach has several drawbacks: (i) All users must trust the third party anonymizer, which is a single point of attack. (ii) A large number of cooperating, trustworthy users is needed. (iii) Privacy is guaranteed only for a single snapshot of user locations; users are not protected against correlation attacks (e.g., history of user movement). We propose a novel framework to support private location-dependent queries, based on the theoretical work on Private Information Retrieval (PIR). Our framework does not require a trusted third party, since privacy is achieved via cryptographic techniques. Compared to existing work, our approach achieves stronger privacy for snapshots of user locations; moreover, it is the first to provide provable privacy guarantees against correlation attacks. We use our framework to implement approximate and exact algorithms for nearest-neighbor search. We optimize query execution by employing data mining techniques, which identify redundant computations. Contrary to common belief, the experimental results suggest that PIR approaches incur reasonable overhead and are applicable in practice. The increasing availability of location-aware mobile devices has given rise to a flurry of location-based services (LBSs). Due to the nature of spatial queries, an LBS needs the user position in order to process her requests. On the other hand, revealing exact user locations to a (potentially untrusted) LBS may pinpoint their identities and breach their privacy. To address this issue, spatial anonymity techniques obfuscate user locations, forwarding to the LBS a sufficiently large region instead. Existing methods explicitly target processing in the euclidean space and do not apply when proximity to the users is defined according to network distance (e.g., driving time through the roads of a city). In this paper, we propose a framework for anonymous query processing in road networks. We design location obfuscation techniques that: (1) provide anonymous LBS access to the users and (2) allow efficient query processing at the LBS side. Our techniques exploit existing network database infrastructure, requiring no specialized storage schemes or functionalities. We experimentally compare alternative designs in real road networks and demonstrate the effectiveness of our techniques. As the use of mobile devices increases, a location-based service (LBS) becomes increasingly popular because it provides more convenient context-aware services. However, LBS introduces problematic issues for location privacy due to the nature of the service. Location privacy protection methods based on k-anonymity and l-diversity have been proposed to provide anonymized use of LBS. However, the k-anonymity and l-diversity methods still can endanger the user's privacy because location semantic information could easily be breached while using LBS. This paper presents a novel location privacy protection technique, which protects the location semantics from an adversary. In our scheme, location semantics are first learned from location data. Then, the trusted-anonymization server performs the anonymization using the location semantic information by cloaking with semantically heterogeneous locations. Thus, the location semantic information is kept secure as the cloaking is done with semantically heterogeneous locations and the true location information is not delivered to the LBS applications. This paper proposes algorithms for learning location semantics and achieving semantically secure cloaking.