Researchers at New York University (NYU), the University of California, Berkeley, and the University of California, San Diego say they have developed the first automated techniques to identify ads potentially tied to human trafficking rings and link them to public information from Bitcoin.
Their approach relies on two novel machine-learning algorithms.
One of the algorithms is based on stylometry. The researchers found they could quickly identify groups of ads with a common author by automating stylometric analysis.
The researchers then tested an automated system that utilizes publicly available information from the Bitcoin mempool and blockchain.
NYU professor Damon McCoy says combining these techniques to identify sex ads by author and Bitcoin owner marks a considerable advancement in assisting law enforcement and nonprofit organizations.
The researchers deployed the automated author identification techniques on a sampling of 10,000 real ads, and reported an 89% true-positive rate for grouping ads by author.
From New York University
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