text: 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.
  @cite_26: Location-based mobile social network services such as foursquare and Gowalla have grown exponentially over the past several years. These location-based services utilize the geographical position to enrich user experiences in a variety of contexts, including location-based searching and location-based mobile advertising. To attract more users, the location-based mobile social network services provide real-world rewards to the user, when a user checks in at a certain venue or location. This gives incentives for users to cheat on their locations. In this paper, we investigate the threat of location cheating attacks, find the root cause of the vulnerability, and outline the possible defending mechanisms. We use foursquare as an example to introduce a novel location cheating attack, which can easily pass the current location verification mechanism (e.g., cheater code of foursquare). We also crawl the foursquare website. By analyzing the crawled data, we show that automated large scale cheating is possible. Through this paper, we aim to call attention to location cheating in mobile social network services and provide insights into the defending mechanisms.
  @cite_9: This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information. This definition includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data. The survey omits non-computational techniques like manually inspecting geotagged photos, and it omits techniques like encryption or access control that treat location data as general symbols. The paper reviews studies of peoples' attitudes about location privacy, computational threats on leaked location data, and computational countermeasures for mitigating these threats.
  @cite_21: Although the privacy threats and countermeasures associated with location data are well known, there has not been a thorough experiment to assess the effectiveness of either. We examine location data gathered from volunteer subjects to quantify how well four different algorithms can identify the subjects' home locations and then their identities using a freely available, programmable Web search engine. Our procedure can identify at least a small fraction of the subjects and a larger fraction of their home addresses. We then apply three different obscuration countermeasures designed to foil the privacy attacks: spatial cloaking, noise, and rounding. We show how much obscuration is necessary to maintain the privacy of all the subjects.
  @cite_0: We present an effective Sybil attack against social location based services. Our attack is based on creating a large number of reputed "bot drivers", and controlling their reported locations using fake GPS reports. We show how this attack can be used to influence social navigation systems by applying it to Waze - a prominent social navigation application used by over 50 million drivers. We show that our attack can fake traffic jams and dramatically influence routing decisions. We present several techniques for preventing the attack, and show that effective mitigation likely requires the use of additional carrier information.
  @cite_5: We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95 of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1 10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
  @cite_16: GeoSocial Networks (GSNs) are online social networks centered on the location information of their users. Users “check-in” their location and use it to acquire location-based special status (e.g., badges, mayorships) and receive venue dependent rewards. The strategy of rewarding user participation however makes cheating a profitable behavior. In this paper we introduce XACT, a suite of venue-oriented secure location verification mechanisms that enable venues and GSN providers to certify the locations claimed by users. We prove that XACT is correct, secure and easy to use. We validate the need for secure location verification mechanisms by collecting and analyzing data from the most popular GSNs today: 780,000 Foursquare users and 143,000 Gowalla users. Through a proof-of-concept implementation on a Revision C4 BeagleBoard embedded system we show that XACT is easy to deploy and economically viable. We analytically and empirically prove that XACT detects location cheating attacks.
summary: Security in Location-based Services. Location-based services face various threats, ranging from rogue users reporting fake GPS @cite_16 @cite_26 , to malicious parties compromising user privacy @cite_5 @cite_21 @cite_9 . A related study on Waze @cite_0 demonstrated that small-scale attacks can create traffic jams or track user icons, with up to 15 mobile emulators. Our work differs in two key aspects. First, we show that it's possible to reverse engineer its APIs, enabling light-weight Sybil devices (simple scripts) to replace full-stack emulators. This increase the scale of potential attacks by orders of magnitude, to thousands of Waze clients per commodity laptop. The impact of thousands of virtual vehicles is qualitatively different from 10-15 mobile simulators. Second , as possible defenses, @cite_0 cites known tools such as phone number IP verification, or location authentication with cellular towers, which have limited applicability (see ). In contrast, we propose a novel proximity graph approach to detect and constrain the impact of virtual devices.