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candidate in computer science, who developed the tools as part of her dissertation. “Is the pimp behind that post for Backpage also behind this post in Craigslist? Is he the same man who keeps receiving Bitcoin for trafficked girls? Questions like these are answerable only through more sophisticated technological tools – exactly what we’ve built in this work – that link ads together using payment mechanisms and the language in Skip Trace the ads themselves.” Portnoff will present the findings this month at the Association for Computing Machinery’s SIGKDD Conference on Knowledge Discovery and Data Mining , one of the world’s leading data-mining conferences, which will publish the paper in its proceedings. The work was funded by Amazon Web Services Cloud Credits for Research program, Giant Oak, Google, the National Science Foundation and the U.S. Department of Education. Computer scientists from UC San Diego and the New York University Tandon School of Engineering were also involved in the study. The research team’s approach relies on two novel algorithms. The first is a machine-learning algorithm rooted in stylometry, which is the analysis of an individual’s writing style to identify authorship. Stylometry can provide confirmation of authorship with high confidence, and, in the case of online trafficking ads, allows researchers and police to identify cases in which separate advertisements for different sex workers share a single author: a telltale sign of a trafficking ring, but hard to identify without sophisticated computer analysis.
For the original version including any supplementary images or video, visit http://news.berkeley.edu/2017/08/16/in-a-step-toward-fighting-human-trafficking-sex-ads-are-linked-to-bitcoin-data/