Bitcoin blockchain analysis could help track down human trafficking victims

Scientists have shown how it is possible to carry out bitcoin blockchain analysis using automated techniques to identify potential victims of human trafficking.

Researchers from a number of universities joined hands to head on a path that they say is the first step towards developing a suite of freely available tools that will enable law enforcement agencies and non-profit organizations track down human trafficking victims and criminals and bring them to justice.

Human trafficking has become a major social menace in the 21st century with an estimated 4.5 million people being forced into sexual exploitation annually. Ironically, one of the major enablers of human trafficking is the Internet – the same medium that has helps knowledge to spread like wildfire.

Human trafficking criminals use the Internet and the deep web to advertise sexual services and while there are issues of tracking down to posted the ads on the deep web, another problem is differentiating between online ads that reflect willing participants in the sex trade and those that reflect victims forced into prostitution.

In the new study, researchers rely on two novel machine learning algorithms. The first is rooted in stylometry, or the analysis of an individual’s writing style to identify authorship. Stylometry can confirm 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 individuals share a single author: a telltale sign of a trafficking ring. 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.

After identifying groups of ads with a single author, the researchers tested an automated system 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, or “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.

The researchers deployed their automated author identification techniques on a sampling of 10,000 real 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 for comparison. They reported an 89 percent true-positive rate for grouping ads by author — significantly more accurate than current stylometric machine learning algorithms. The team also reported a high rate of success in linking the ads they placed themselves to timestamps in the Bitcoin blockchain. They acknowledge, however, 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 – that matter must ultimately be pursued by police.

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