Defending Against Sybil Devices in Crowdsourced Mapping Services

Crowdsourcing is an unique and practical approach to obtain personalized data and content. Its impact is especially significant in providing commentary, reviews and metadata, on a variety of location based services. In this study, we examine reliability of the Waze mapping service, and its vulnerability to a variety of location-based attacks. Our goals are to understand the severity of the problem, shed light on the general problem of location and device authentication, and explore the efficacy of potential defenses. Our preliminary results already show that a single attacker with limited resources can cause havoc on Waze, producing ``virtual'' congestion and accidents, automatically re-routing user traffic, and compromising user privacy by tracking users' precise movements via software while staying undetected. To defend against these attacks, we propose a proximity-based Sybil detection method to filter out malicious devices.