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“Imagine looking through page after page of explicit advertisements, some for underage victims. You’re looking through all this material to find the set that are advertising trafficked and underage victims. Even given a team of humans dedicated to the task, there’s simply too much data – often quite traumatizing – to go through,” Portnoff said. By automating stylometric analysis, the researchers discovered they could quickly identify groups of ads with a common author on Backpage, one of the most popular sites for online sex ads. (Since this research was conducted, the adult advertising section of Backpage was discontinued; however, the researchers noted that adult ads remain prevalent, now appearing in multiple sections of the site.) After identifying groups of ads with a single author, the researchers then tested a second algorithm that utilizes publicly available information from the Bitcoin mempool and blockchain — the ledgers that record pending and completed transactions. Because Backpage posts ads as soon as payment is received, the researchers compared the timestamp indicating submission of payment to the timestamp of the ads’ appearance on Backpage. All Bitcoin users maintain accounts, called wallets, and tracing payment of ads that have the same author to a unique wallet is a potential method for identifying ownership of the ads, and thus the individuals or groups involved in human trafficking. “There are hundreds of thousands of these ads placed every year, and any technique that can surface commonalities between ads and potentially shed light on the owners is a big boost for those working to curb exploitation,” said Damon McCoy , an NYU Tandon assistant professor of computer science and engineering and one of the paper’s co-authors. Combining automated stylometric and timestamp analysis to identify sex ads by both author and Bitcoin owner represents a considerable advancement in assisting law enforcement and nonprofit organizations that try to identify victims of human trafficking, McCoy said. The researchers deployed their automated author identification techniques on a sampling of 10,000 real adult ads on Backpage, a four-week scrape of all adult ads that appeared on Backpage during that time, as well as on several dozen ads they themselves placed as a point of comparison.
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/
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