Biblio

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. To defend against Sybil devices, we propose a new approach 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 how they can be used to dramatically reduce the impact of the attacks. We have informed Waze/Google team of our research findings. Currently, we are in active collaboration with Waze team to improve the security and privacy of their system.

This paper introduces SONA (Spatiotemporal system Organized for Natural Analysis), a tabletop and tangible controller system for exploring geotagged information, and more specifically, CCTV. SONA's goal is to support a more natural method of interacting with data. Our new interactions are placed in the context of a physical security environment, closed circuit television (CCTV). We present a three-layered detail on demand set of view filters for CCTV feeds on a digital map. These filters are controlled with a novel tangible device for direct interaction. We validate SONA's tangible controller approach with a user study comparing SONA with the existing CCTV multi-screen method. The results of the study show that SONA's tangible interaction method is superior to the multi-screen approach, both in terms of quantitative results, and is preferred by users.

Participatory sensing tries to create cost-effective, large-scale sensing systems by leveraging sensors embedded in mobile devices. One major challenge in these systems is to protect the users' privacy, since users will not contribute data if their privacy is jeopardized. Especially location data needs to be protected if it is likely to reveal information about the users' identities. A common solution is the blinding out approach that creates so-called ban zones in which location data is not published. Thereby, a user's important places, e.g., her home or workplace, can be concealed. However, ban zones of a fixed size are not able to guarantee any particular level of privacy. For instance, a ban zone that is large enough to conceal a user's home in a large city might be too small in a less populated area. For this reason, we propose an approach for dynamic map-based blinding out: The boundaries of our privacy zones, called Silent Zones, are determined in such way that at least k buildings are located within this zone. Thus, our approach adapts to the habitat density and we can guarantee k-anonymity in terms of surrounding buildings. In this paper, we present two new algorithms for creating Silent Zones and evaluate their performance. Our results show that especially in worst case scenarios, i.e., in sparsely populated areas, our approach outperforms standard ban zones and guarantees the specified privacy level.