Poster: Defending against Sybil Devices in Crowdsourced Mapping Services

By G. Wang, Bolun Wang, Tianyi Wang, A. Nika, Haitao Zheng, Ben Y. Zhao. 2016

25 citations recorded by the source corpus.

Crowdsourcing is a 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.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chris J. M. Yoon
2. Clement Fung
3. Ivan Beschastnikh
4. Dusist Niyato
5. Zehui Xiong
6. Jiawen Kang

In Information Systems

1. Chris J. M. Yoon
2. Clement Fung
3. Ivan Beschastnikh

Citations in the corpus

• Mitigating Sybils in Federated Learning Poisoning (2018, background): “Some approach actively detect and remove sybils using auxiliary information, such as an underlying social network [38], or detect and reject malicious behavior [39], [40].”