Defending against Sybil Devices in Crowdsourced Mapping Services

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 {\em 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 {\em 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 {\em 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.Comment: Measure and integratio

Internet authentication based on personal history - a feasibility test

On the Internet, there is an uneasy tension between the security and usability of authentication mechanisms. An easy three-part classification is: 'something you know' (e.g. password); 'something you hold' (e.g. device holding digital certificate), and 'who you are' (e.g. biometric assessment) [9]. Each of these has well-known problems; passwords are written down, guessable, or forgotten; devices are lost or stolen, and biometric assays alienate users. We have investigated a novel strategy of querying the user based on their personal history (a 'Rip van Winkle' approach.) The sum of this information is large and well-known only to the individual. The volume is too large for impostors to learn; our observation is that, in the emerging environment, it is possible to collate and automatically query such information as an authentication test. We report a proof of concept study based on the automatic generation of questions from electronic 'calendar' information. While users were, surprisingly, unable to answer randomly generated questions any better than impostors, if questions are categorized according to appropriate psychological parameters then significant results can be obtained. We thus demonstrate the potential viability of this concept