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Data for Social Good Case Study: Global Emancipation Network

Author: Kendra Schreiner
Published Date: 2 July 2018

AboutGlobal Emancipation Network (GEN) fights human trafficking using big data analytics and intelligence techniques. Like many other criminal enterprises, traffickers are reliant on internet-based applications to find and groom victims, organise transportation, and advertise their victims’ services. This data can be collected and analysed to track and stop traffickers as well as to identify and rescue victims. GEN aims to be the global repository for trafficking data worldwide and to facilitate communication and technology-sharing between numerous anti-trafficking stakeholders across the globe.

GEN’s data and analytics platform, Minerva, is available free of charge to users, which include law enforcement, government agencies, researchers, the commercial sector, academia, and counter-trafficking non-profits, so that they can combine it with their own datasets. GEN aims to empower users to concentrate on achieving their specific missions without worrying about resources and technology. GEN also works with counter-trafficking stakeholders to improve their understanding of trafficking data and applicable technologies.

Partners and fundersSplunk4Good, the charitable arm of data analytics company Splunk, provided technology from its $100million global pledge and continues to work with GEN. Microsoft Philanthropies provides Azure compute resources and storage, which GEN uses to collect and store data that is then processed by Splunk. GEN recently partnered with Deep Vision AI to deploy visual recognition tools into GEN’s analytics platform to further enhance the image analysis tools provided by Microsoft Cognitive APIs. Other technology partners include Chainalysis, Recorded Future, Dark Owl, GitHub, Maltego, Neustar, and more. GEN also partners with law enforcement, attorneys general, NGOs, and academia.


●    Measuring the scale of human trafficking is difficult and methods vary widely. NGOs, law enforcement, and academia typically do not work together to reach accurate numbers. There are many data silos and previously there has not been an easy way to share data.

●   Data handling and privacy laws make collaboration difficult at times, particularly with government agencies. Well-intentioned laws often handicap investigations and public-private collaborations.

●    The nature of criminal enterprises ensures a constantly evolving, dynamic data landscape that requires constant management of data collection tools and pipeline.

The Data. GEN uses a data analytics platform called Minerva, which enables secure, individualised data sharing and the application of intelligent analytics to reveal implicit, previously unknown information from large amounts of data. Trafficking data is scraped from both the open and deep web. The data is analysed for suspicious signals by analysing keywords, phone numbers, geographic data, or usernames. The platform studies the language used in online ads, as specific words signal different victims, for example ‘fresh’ can be a signal for a young girl. Visual intelligence from Deep Vision and Microsoft Cognitive APIs use artificial intelligence to enable real-time insights into image sets and video feeds, reducing the time it takes to find relevant images. GEN is able to analyse even blockchain, which is supposed to be completely anonymous. Backpage (a buy/sell website implicated in 73% of all child trafficking reports in the U.S.) ads are purchased with Bitcoin, and the time that new ads are purchased can be correlated with deductions from bitcoin wallets, which basically de-anonymises the transaction. Backpage has been seized and shut down by the FBI, but other sites use cryptocurrencies like Bitcoin.

GEN have gathered thousands of trafficking cases and convictions as well as information on international legislation. This data is then enriched with public records and open web searches and through image analysis tools, and partners can combine GEN’s data with their own specialised datasets.

Enabling Factors. Unlike many organisations fighting trafficking, GEN staff are tech savvy - many staff come from the cyber threat intelligence field and are applying the same methods and techniques to this field.

Role of Philanthropy. Philanthropy has been involved through both grants and the provision of data analytics and software. Splunk not only provided funding but has been a foundational technology and operational partner. This relationship came about through a personal connection with the VP, Head of Security Research at Splunk, Monzy Merza, who had a personal interest in ending trafficking. Splunk4Good had been searching for a partner in this space for some time, and GEN’s approach was a great fit for their product and expertise. They’ve continued a close collaboration and the impact has been profound, according to GEN CEO Sherrie Caltagirone. Similarly, Microsoft Philanthropy donated its Azure technology, and many other technology partners provide services on a pro bono basis.

Impact. The data and platform are used by nonprofits, law enforcement, government, the financial sector, and others. Minerva has also been tested with a U.S. District Attorney and to study whether site shutdowns prevent bad behaviour, finding that it does not as users simply go elsewhere. GEN’s platform is helping track and predict where victims might go and what routes traffickers appear to be using. Within the first week of its incorporation into GEN’s platform, Deep Vision’s facial recognition tools led to the identification of an individual and a network of potential traffickers and victims; the case has been referred to law enforcement. To date, 989 traffickers, pimps, and victims have been identified using Minerva and over 22,000 individuals are currently being tracked in the tool.


This case study is part of a larger project on the role of philanthropy in using data to solve complex problems. A global scan highlights many initiatives using data for good, the main methods, how philanthropy is engaging, and the challenges faced. 


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