Title: Backpage and Bitcoin: Uncovering Human Traffickers

Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unmeasurable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very dicult task. Very little concrete truth (i.e., ads known to be posted by traffickers) 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 classier that uses stylometry to distinguish between ads posted by the same vs. dierent 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 end human trafficking.

Bio:

Sadia Afroz, PhD, is a research scientist at the International Computer Science Institute (ICSI). Her work focuses on anti-censorship, anonymity and adversarial learning. Her work on adversarial authorship attribution received the 2013 Privacy Enhancing Technology (PET) award, the best student paper award at the 2012 Privacy Enhancing Technology Symposium (PETS) and the 2014 ACM SIGSAC dissertation award (runner-up). More about her research can be found: http://www1.icsi.berkeley.edu/~sadia/