To fight human trafficking, UC Berkeley researcher links sex ads to Bitcoin data
Source: Mengjie
Rebecca Portnoff, a doctoral candidate in computer science at University of California, Berkeley, has developed the first automated techniques to identify adult ads tied to human trafficking rings by linking the ads to public information from Bitcoin, a cryptocurrency.
As websites for online classified ads selling sex are widely used by human traffickers, and Bitcoin is the primary payment method for online sex ads, Portnoff's effort is seen as a first step toward developing a suite of freely available tools to help police and non-profit institutions identify victims of sexual exploitation on websites.
Such websites include Backpage and Craigslist, where ads for human trafficking are often found.
Without the new tools, law enforcement efforts to trace and disband human trafficking rings are hindered by the pseudonymous nature of adult ads, the tendency of ring leaders to employ multiple phone numbers and email addresses to avoid detection and the difficulty in determining which online ads reflect willing participants in the sex trade and which reflect victims forced into prostitution.
"The technology we've built finds connections between ads," explained Portnoff, 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 the ads themselves."
Presented at the Association for Computing Machinery's SIGKDD Conference on Knowledge Discovery and Data Mining from August 13 to 17 in Halfiax, Nova Scotia, Canada, one of the world's leading data-mining conferences, the work relies on a machine-learning algorithm rooted in stylometry, which is the analysis of an individual's writing style to identify authorship; and then on a second algorithm that utilizes publicly available information from the Bitcoin mempool and blockchain, the ledgers that record pending and completed transactions.
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. And, as all Bitcoin users maintain accounts, called wallets, tracing payment of ads that have the same author to a unique wallet could help identify ownership of the ads, and thus the individuals or groups involved in human trafficking.
Portnoff and her collaborators 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 the advertising website during that time, as well as on several dozen ads they themselves placed as a point of comparison. They reported an 89 percent true-positive rate for grouping ads by author and a high rate of success in linking the ads they placed themselves to the corresponding transactions in the Bitcoin blockchain.
However, according to a news release from UC Berkeley, the researchers acknowledged that they were unable to verify whether matches they made using real-life ads and Bitcoin transaction information truly correspond to individuals tied to human trafficking, because that must ultimately be pursued by police.
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