Backpage and bitcoin

Uncovering human traffickers

Rebecca S. Portnoff, Danny Yuxing Huang, Periwinkle Doerfler, Sadia Afroz, Damon McCoy

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

    Abstract

    Sites for onlineclassified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.

    Original languageEnglish (US)
    Title of host publicationKDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
    PublisherAssociation for Computing Machinery
    Pages1595-1604
    Number of pages10
    VolumePart F129685
    ISBN (Electronic)9781450348874
    DOIs
    StatePublished - Aug 13 2017
    Event23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017 - Halifax, Canada
    Duration: Aug 13 2017Aug 17 2017

    Other

    Other23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
    CountryCanada
    CityHalifax
    Period8/13/178/17/17

    Fingerprint

    Learning systems
    Marketing
    Sales
    Classifiers
    Concretes
    Industry

    ASJC Scopus subject areas

    • Software
    • Information Systems

    Cite this

    Portnoff, R. S., Huang, D. Y., Doerfler, P., Afroz, S., & McCoy, D. (2017). Backpage and bitcoin: Uncovering human traffickers. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Vol. Part F129685, pp. 1595-1604). Association for Computing Machinery. https://doi.org/10.1145/3097983.3098082

    Backpage and bitcoin : Uncovering human traffickers. / Portnoff, Rebecca S.; Huang, Danny Yuxing; Doerfler, Periwinkle; Afroz, Sadia; McCoy, Damon.

    KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F129685 Association for Computing Machinery, 2017. p. 1595-1604.

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

    Portnoff, RS, Huang, DY, Doerfler, P, Afroz, S & McCoy, D 2017, Backpage and bitcoin: Uncovering human traffickers. in KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. vol. Part F129685, Association for Computing Machinery, pp. 1595-1604, 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017, Halifax, Canada, 8/13/17. https://doi.org/10.1145/3097983.3098082
    Portnoff RS, Huang DY, Doerfler P, Afroz S, McCoy D. Backpage and bitcoin: Uncovering human traffickers. In KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F129685. Association for Computing Machinery. 2017. p. 1595-1604 https://doi.org/10.1145/3097983.3098082
    Portnoff, Rebecca S. ; Huang, Danny Yuxing ; Doerfler, Periwinkle ; Afroz, Sadia ; McCoy, Damon. / Backpage and bitcoin : Uncovering human traffickers. KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Vol. Part F129685 Association for Computing Machinery, 2017. pp. 1595-1604
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