Defending against Sybil Devices in Crowdsourced Mapping Services

Abstract

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, colocation 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. of this attack against our own vehicles, quantifying the accuracy of the attack against GPS coordinates. Magnified by an army of ghost riders, an attacker can potentially track the constant whereabouts of millions of users, all without any risk of detection. Defenses.Prior proposals to address the location authentication problem have limited appeal, because of reliance on widespread deployment of specialized hardware, either as part of physical infrastructure, i.e., cellular base stations, or as modifications to mobile devices themselves. Instead, we propose a practical solution that limits the ability of Sybil devices to amplify the potential damage incurred by any single attacker. We introduce collocation edges, authenticated records that attest to the one-time physical proximity of a pair of mobile devices. The creation of collocation edges can be triggered opportunistically by the mapping service, e.g., Waze. Over time, collocation edges combine to form large proximity graphs, network structures that attest to physical interactions between devices. Since ghost riders cannot physically interact with real devices, they cannot form direct edges with real devices, only indirectly through a small number of real devices operated by the attacker. Thus, the edges between an attacker and the rest of the network are limited by the number of real physical devices she has, regardless of how many ghost riders are under her control. This reduces the problem of detecting ghost riders to a community detection problem on the proximity graph (The graph is seeded by a small number of trusted infrastructure locations).Our paper includes these key contributions:

Exploiting Proximity-Based Mobile Apps for Large-Scale Location Privacy Probing

“…It is not efficient [24][25][26]. For example, at to the NS app, one may randomize some items of his/her profiles (e.g., nickname, user ID) to disconnect the linkage between user IDs and locations each time when he/she looks for nearby strangers [27], hence preventing an attacker from inferring the privacy even if the location is leaked.…”

Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

“…AnonySense was one of the earliest solutions that utilize group signatures for crowdsensing. As pointed in [59], the way AnonySense [28] employs group signatures renders it vulnerable to Sybil attacks [60]. Because in AnonySense, it is impossible to identify signatures from the same participant, without opening the signatures of all data reports.…”

“…In addition, SRBE makes typical Sybil attacks [60,76] harder on GROUPSENSE, by restricting participants to use one pseudoID at a given time interval. In Sybil attack [60], a participatory node illegitimately claims multiple identities.…”

Privacy Risks in Vehicle Grids and Autonomous Cars

“…By moving the window, the attacker tracks the victim. In the Waze DDoS Attack, the malicious customer impersonates multiple WAZE vehicles in a small area, simulating traffic bottleneck [12].…”