Using Technologies to Uncover Patterns in Human Trafficking

Author: Szakonyi, Annamaria; Chellasamy, Harshini; Vassilakos, Andreas & Dawson, Maurice

Abstract: In this paper, the researchers provide a background of human trafficking, review the use and implications of digital currency, and apply machine learning techniques to analyze publicly available trafficking datasets. The study also provides recommendations related to data collection, management, and analysis to aid the vital fight against individuals’ exploitation. The researchers conducted an exploratory data analysis using Python, RapidMiner, and Microsoft Excel towards an iterative review and interpretation of the dataset from the Counter Trafficking Data Collaborative (CTDC). The researchers found that there are more female victims of human trafficking in most age groups than male victims. However, for the age group between 39–47, there was a higher male victim count. Additionally, researchers found that the top five countries affected by human trafficking were the Philippines, Ukraine, Republic of Moldova, USA, and Cambodia. However, it must be noted that there are limitations to the overall data because they are provided voluntarily by organizations, and therefore, there is no equitable distribution of actual results from all countries and players. After mapping the country of origin and country of exploitation, it was made clear that there is a movement of victims from the country of origin to the country of exploitation. Lastly, researchers found that a complex combination of different variables is needed to provide accurate predictions for law enforcement and anti-trafficking organizations to aid them in fighting human trafficking, including country of exploitation and type of exploitation being the most important features in the prediction.

Keywords: human trafficking, law enforcement, dark web, metadata, blockchain, bitcoin, cryptocurrency, data analysis, data science, machine learning, technology, gender