Real-time crowdsourced maps such as Waze provide timely updates on traffic, congestion, accidents and points of interest. In this paper, we demonstrate how lack of strong location authentication allows creation of software-based Sybil devices that expose crowdsourced map systems to a variety of security and privacy attacks. Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. More importantly, we describe techniques to generate Sybil devices at scale, creating armies of virtual vehicles capable of remotely tracking precise movements for large user populations while avoiding detection. We propose a new approach to defend against Sybil devices based on co-location edges , authenticated records that attest to the one-time physical co-location of a pair of devices. Over time, co-location edges combine to form large proximity graphs that attest to physical interactions between devices, allowing scalable detection of virtual vehicles. We demonstrate the efficacy of this approach using large-scale simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.
Mobile Location Authentication. Defending against forged GPS is challenging. One direction is to authenticate user locations using wireless infrastructures: WiFi APs @cite_56 @cite_29 , cellular base stations @cite_56 @cite_29 and femtocells @cite_11 . Devices must come into physical proximity to these infrastructures to be authenticated. But it requires cooperation among a wide range of infrastructures (also modifications to their software hardware), which is impractical for large-scale services like Waze. Our work only uses a small number of trusted infrastructures to bootstrap, and relies on peer-based trust propagation to achieve coverage. Other researchers have proposed peer-based'' methods to authenticate collocated mobile devices @cite_32 @cite_33 @cite_7 @cite_4 @cite_34 . Different from existing work, we use peer-based collocation authentication to build proximity graphs for Sybil detection, instead of directly authenticating a device's physical location.
@cite_4 @cite_33 @cite_7 @cite_29 @cite_32 @cite_56 @cite_34 @cite_11
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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. Small, mobile devices without user interfaces, such as Bluetooth headsets, often need to communicate securely over wireless networks. Active attacks can only be prevented by authenticating wireless communication, which is problematic when devices do not have any a priori information about each other. We introduce a new method for device-to-device authentication by shaking devices together. This paper describes two protocols for combining cryptographic authentication techniques with known methods of accelerometer data analysis to the effect of generating authenticated, secret keys. The protocols differ in their design, one being more conservative from a security point of view, while the other allows more dynamic interactions. Three experiments are used to optimize and validate our proposed authentication method. 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.