U.K.-U.S. prize challenges

At Summit for Democracy, the United Kingdom and the United States Announce Winners of Challenge to Drive Innovation in Privacy-Enhancing Technologies That Reinforce Democratic Values.

Transforming financial crime prevention and boosting pandemic response capabilities through privacy-preserving federated learning

Click here to watch the winners being announced at the Summit for Democracy.

The winning solutions combined different PETs to allow the AI models to learn to make better predictions without exposing any sensitive data.

World-leading experts competed for cash prizes from a combined UK-U.S. prize pool of £1.3 m / $1.6m.

The prizes encouraged the development of innovative solutions that address practical data privacy concerns in real world scenarios. 

Further information is available in the press release

Read the blog post from DrivenData here and the UK Centre for Data Ethics and Innovation and Innovate UK here.

Prize challenges have now closed. 

Innovating to tackle global challenges

Privacy-enhancing technologies (PETs) have the potential to help us devise data-driven, innovative solutions to tackle the most pressing global societal challenges we're facing, while preserving citizens’ fundamental right to privacy, which constitutes a foundation for democratic societies. 

By enabling organisations to share and collaboratively analyse sensitive data in a privacy-preserving manner, PETs open up unprecedented opportunities to harness the power of data through innovative and trustworthy applications.

UK-US collaboration

The United States and United Kingdom will continue to build on their shared interest in advancing responsible innovation in PETs. In May, a joint Demo Day will be held in London to deepen transatlantic communities of practice among UK and U.S. privacy researchers and government representatives. Further collaboration in this space, such as developing tools and guidance to assist practitioners to adopt these technologies effectively and responsibly, is being actively explored.

The goals of the challenges

Drive innovation in the technological development and application of novel PETs

Deliver strong end-to-end privacy guarantees against a set of common threats and privacy attacks, leveraging a combination of input and output privacy techniques

Develop a privacy-preserving solution that is capable of efficiently generating high-utility machine learning models for one of two predefined use-cases in finance and public health, detailed below

The UK and U.S winners of the prize challenges are listed below:

UK winners

Final Winners:

First place (joint): University of Cambridge

First place (joint):  STARLIT (Privitar, University College London, Cardiff University)

Third place: Faculty

Fourth place: Featurespace

Special recognition prizes: 

Diagonal
Faculty
Featurespace
Privitar
University of Liverpool

Red team winner:

Trūata

White paper prizes:

Faculty, Featurespace, STARLIT (Privitar, University College London, Cardiff University), University of Cambridge, University of Liverpool, DeepMind and OpenMined*, Corvus Research Limited, Diagonal Works, GMV, Privately SA 

(*DeepMind and OpenMined chose not to accept any prize funds for this challenge.)

US Winners 

Final Winners:

Track A: Financial Crime Prevention 

Scarlet Pets (Rutgers University) 

PPML Huskies (University of Washington Tacoma, Delft University of Technology, University of Brasilia)

ILLIDAN Lab (Michigan State University, University of Calgary)

Track B: Pandemic Response and Forecasting

puffle (Carnegie Mellon University)

MusCAT (Broad Institute, MIT, Harvard Business School, University of Texas Austin, University of Toronto)

ZS_RDE_AI (ZS Associates) 

Special Recognition: 

Visa Research

Red Team Winners:

ETH SRI (ETH Zurich) 

Entmoot (Independent researcher)

Blackbird Labs

White Paper Prizes: 

MusCAT (Broad Institute, MIT, Harvard Business School, University of Texas Austin, University of Toronto), IBM Research, Secret Computers (Inpher Inc) 

Find profiles of the winners from the UK challenge here

Find profiles of the winners from the U.S. challenge here 

Structure of the prize challenges

The challenges, which were free to enter, took the form of a multi-stage competition involving a white paper submission, prototype development, and a red-teaming phase.

Participants could select one track or both tracks, or for extra points, develop a solution that works for both.

Track 1 
Transforming financial crime prevention

Innovators were asked to develop solutions that help tackle the challenge of international money laundering, which finances organized crime including human trafficking and terrorist financing, and undermines economic prosperity – costing up to US$2 trillion each year, according to UN estimates.

This illicit activity could be more effectively identified through information sharing and collaborative analytics among financial organisations, but such approaches are made more challenging by legal and technical requirements to ensure customer privacy. Organisations including the Financial Action Task Force have highlighted the potential of PETs to help tackle these barriers by enabling privacy-preserving access to data.

Innovators were asked to develop end-to-end privacy-preserving federated learning solutions to detect potentially anomalous payments, leveraging a combination of input and output privacy techniques. To develop solutions, innovators used synthetic datasets created by Swift, the global provider of secure financial messaging services.

While developing the solutions, innovators in the U.K. were able to engage with the Information Commissioner’s Office (ICO), the U.K. National Economic Crime Centre, and the Financial Conduct Authority (FCA), and innovators in the U.S. were able to engage with the Financial Crimes Enforcement Network (FinCEN).

Find the technical briefs for Track 1 here

Track 2 
Forecasting to bolster pandemic response capabilities

Innovators were asked to bolster pandemic response capabilities in both the United States and United Kingdom by developing privacy-preserving federated learning solutions to improve forecasting. The COVID-19 pandemic - which has incurred an immense human cost and socio-economic impact across the globe - demonstrated the importance of preparing for public health emergencies by harnessing the power of data through privacy-preserving data sharing and analytics.

Innovators were asked to develop privacy-preserving federated learning solutions to forecast an individual’s risk of infection, leveraging a combination of input and output privacy techniques. Participants had access to a synthetic dataset created by the University of Virginia’s Biocomplexity Institute, which represented a digital twin of a population with statistical and dynamical properties similar to a real population.

While developing the solutions, innovators in the U.K. were able to engage with the Information Commissioner’s Office (ICO), NHS England, and the UKRI-funded Data and Analytics Research Environments UK (DARE UK), and innovators in the U.S. were able to engage with staff from the Centers for Disease Control and Prevention (CDC).

Find the technical briefs for Track 2 here

The prize challenges have been developed as part of a joint effort between the United Kingdom and the United States.

The prize challenges have been developed as part of a joint effort between the United Kingdom and the United States.

The challenges have been led by the U.K.’s Centre for Data Ethics and Innovation (CDEI) and Innovate UK, the U.S. National Institute of Standards and Technology (NIST), and the U.S. National Science Foundation (NSF), in cooperation with the White House Office of Science and Technology Policy.

We are grateful for the involvement of Data and Analytics Research Environments UK (DARE UK), Financial Conduct Authority (FCA), Financial Crimes Enforcement Network (FinCEN), Information Commissioner’s Office (ICO), the National Crime Agency (NCA), NHS England, the University of Virginia’s Biocomplexity Institute, and Swift. We are also grateful for the advice we have received from experts across our governments and among civil society.

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