TY - GEN
T1 - Backpage and bitcoin
T2 - 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017
AU - Portnoff, Rebecca S.
AU - Huang, Danny Yuxing
AU - Doerfler, Periwinkle
AU - Afroz, Sadia
AU - McCoy, Damon
N1 - Funding Information:
This work was supported in part by the National Science Foundation under grant CNS-1619620, by the Amazon “AWS Cloud Credits for Research”, by the U.S. Department of Education, by Giant Oak, and by gifts from Google and Thorn. We thank all the people that provided us with assistance in analysis; in particular we thank Chainalysis for the use of their tools and their contribution to this work. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsors.
Publisher Copyright:
© 2017 Copyright held by the owner/author(s).
PY - 2017/8/13
Y1 - 2017/8/13
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85029045814&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029045814&partnerID=8YFLogxK
U2 - 10.1145/3097983.3098082
DO - 10.1145/3097983.3098082
M3 - Conference contribution
AN - SCOPUS:85029045814
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 1595
EP - 1604
BT - KDD 2017 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
Y2 - 13 August 2017 through 17 August 2017
ER -