Big Data and the prevention of Child Sexual Exploitation

29 January, 2019

Matt Burns

The potential for big data to unlock crucial insights for law enforcement agencies (LEAs) is huge. “Predictive policing” is becoming increasingly mainstream as police forces across the globe leverage their vast data stores in order to analyse crime trends.

Road network maps and weather data, for example, helped the police department in Manchester, New Hampshire, to significantly reduce the amount of thefts in the area – in fact, a 32% drop in thefts from vehicles was achieved. But can the same approach be applied to the investigation of child sexual exploitation (CSE)?

Focusing resources

The economic landscape facing LEAs in the UK and further afield is an ever-present challenge. With stretched resources, investigators have been forced to work smarter in order to maximise their output – and big data represents a significant opportunity to do just that. A number of British councils subscribe to this view and have created algorithms which help to focus their output on children who are at the highest risk of being exploited.

Rather than allowing LEAs to be “predictive” in the truest sense of the word, in most cases, big data allows them to analyse when, where and how child sexual exploitation occurs. By cross-referencing factors such as the criminal records of parents, school attendance rates and hospital admissions, for example, LEAs can target their resources towards those who need them most.

Potential stumbling blocks

This has, nevertheless, proved controversial in some quarters. Data privacy concerns, as well as the risk of inserting biases against particular social classes and ethnicities have emerged as key counterarguments against wider usage of big data. Detractors fear that these perceived flaws will prevent against the safeguarding of children at risk in “untraditional” spheres and instead reinforce unhelpful stereotypes.

However, big data can also be leveraged to highlight these exact biases in existing records. By doing so, LEAs can proactively compensate for them by establishing outreach programmes to children in demographics which have historically gone under the radar. This would have the effect of broadening the reach of law enforcement in the fight against CSE, rather than narrowing it.

Internet Crimes against Children

Internet crimes against children (ICAC) form an increasingly significant component of the battle against CSE. Recently, industry experts even warned that we are in the midst of a “global pandemic” when it comes to the global spread of CSE images. Big data is also extremely useful in the fight against this.
CameraForensics are using big data to link cameras implicated in the online exploitation of children, enabling LEAs to find victims faster. A vast database of over 2 billion images compiled from the open internet identifies photo similarities through defining characteristics such as geographic location or metadata. This builds up a solid statistical picture of what “normal” and “abnormal” images comprise of.

Griffeye’s digital forensics tools provide a further helping hand to investigators in the fight against ICAC. The Brains programme, for example, uses artificial intelligence (AI) to help those on the ground to prioritise the enormous data stores available to them. Rather than been mired in irrelevant material, this will help them to access the right information at the right time.

By leveraging these tools, LEAs can make those all-important connections between covert and public personas, helping to prevent prolonged trauma to victims and bringing perpetrators to justice more swiftly.

Helping victims sooner

LEAs are fighting incredibly complex battles on several fronts with limited resources. But in numerous ways, big data empowers investigators to safeguard victims and prosecute perpetrators of CSE more quickly.

Although LEAs will not be able to predict and prevent every incidence of abuse even with the power of big data behind them, they will be able to operate more efficiently and more effectively, targeting their resources towards those in greatest need.