You can yak but you can't hide: Localizing anonymous social network users

Minhui Xue, Cameron Ballard, Kelvin Liu, Carson Nemelka, Yanqiu Wu, Keith Ross, Haifeng Qian

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    The recent growth of anonymous social network services - such as 4chan, Whisper, and Yik Yak - has brought online anonymity into the spotlight. For these services to function properly, the integrity of user anonymity must be preserved. If an attacker can determine the physical location from where an anonymous message was sent, then the attacker can potentially use side information (for example, knowledge of who lives at the location) to de-Anonymize the sender of the message. In this paper, we investigate whether the popular anonymous social media application Yik Yak is susceptible to localization attacks, thereby putting user anonymity at risk. The problem is challenging because Yik Yak application does not provide information about distances between user and message origins or any other message location information. We provide a comprehensive data collection and supervised machine learning methodology that does not require any reverse engineering of the Yik Yak protocol, is fully automated, and can be remotely run from anywhere. We show that we can accurately predict the locations of messages up to a small average error of 106 meters. We also devise an experiment where each message emanates from one of nine dorm colleges on the University of California Santa Cruz campus. We are able to determine the correct dorm college that generated each message 100% of the time.

    Original languageEnglish (US)
    Title of host publicationIMC 2016 - Proceedings of the 2016 ACM Internet Measurement Conference
    PublisherAssociation for Computing Machinery
    Pages25-31
    Number of pages7
    ISBN (Electronic)9781450345262
    DOIs
    StatePublished - Nov 14 2016
    Event2016 ACM Internet Measurement Conference, IMC 2016 - Santa Monica, United States
    Duration: Nov 14 2016Nov 16 2016

    Publication series

    NameProceedings of the ACM SIGCOMM Internet Measurement Conference, IMC
    Volume14-16-November-2016

    Other

    Other2016 ACM Internet Measurement Conference, IMC 2016
    CountryUnited States
    CitySanta Monica
    Period11/14/1611/16/16

    Keywords

    • Anonymous Social Networks
    • Localization Attack
    • Machine Learning Inference
    • Yik Yak

    ASJC Scopus subject areas

    • Software
    • Computer Networks and Communications

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