Attacks and Defenses in Crowdsourced Mapping Services

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Abstract
Compared to traditional online maps, crowdsourced maps such as Waze are unique in providing real-time updates on traffic, congestion, accidents and points of interest. In this paper, we explore the practical impact of attacks against crowdsourced map systems, and develop robust defenses against them. Our experiments show that a single attacker with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic. We describe techniques to emulate Waze-enabled vehicles using lightweight scripts, and how to use these "ghost riders" to compromise user privacy by remotely tracking precise user movements while avoiding detection. A single attacker can control groups of ghost riders, overwhelming data from legitimate users and magnifying the impact of attacks. As defense, we propose a new approach 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}, network that attest to physical interactions between devices. "Ghost-riders" cannot physically interact with real devices and can be detected using graph algorithms. We demonstrate the efficacy of this approach using large simulations, and discuss how they can be used to dramatically reduce the impact of attacks against crowdsourced mapping services.

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