- The UK and US announced winners of the Privacy-Enhancing Technologies (PETs) prize challenges at the second Summit for Democracy.
- Focused on the collaborative development of AI models while keeping sensitive information private, the challenges addressed pandemic forecasting and financial crime detection.
- A combined prize pool of $1.6 million (£1.3 million) was awarded to winners whose innovative solutions combined different PETs.
- The winning solutions withstood “red team” tests to reveal their resilience in protecting original data.
- A joint Demo Day in London in May will further collaboration between UK and US privacy researchers and government representatives.
Innovating for a Privacy-Centric Future
Yesterday, at the second Summit for Democracy, the United States and the United Kingdom announced the winners of their joint prize challenges aimed at driving innovation in Privacy-Enhancing Technologies (PETs) to reinforce democratic values. Inaugurated in December 2021, the challenges inspired innovators across the Atlantic to build solutions that enable the collaborative development of artificial intelligence (AI) models while keeping sensitive information private. The challenges focused on PETs solutions for two scenarios: forecasting pandemic infection and detecting financial crime.
United in the Pursuit of Data Privacy
Participants from academic institutions, global technology companies, and privacy start-ups competed for cash prizes from a combined UK-US prize pool of $1.6 million (£1.3 million). The winning solutions combined different PETs to allow AI models to learn and make better predictions without exposing sensitive data. This approach encouraged the development of innovative solutions that address practical data privacy concerns in real-world scenarios.
In the final phase of the challenges, the privacy guarantees of the solutions were put to the test by “red teams” attempting to reveal the original data used for training the models. The resilience of the solutions to these attacks determined the final winners. UK participants also received support from the UK Information Commissioner’s Office (ICO) to help them consider how their solutions could demonstrate compliance with key UK data protection regulation principles.
Collaboration and Responsible Innovation
The US and UK will continue building 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 US privacy researchers and government representatives. Further collaboration in this space, such as developing tools and guidance to assist practitioners in adopting these technologies effectively and responsibly, is actively being explored.
Michelle Donelan, Secretary of State for the UK Department for Science, Innovation and Technology, emphasized the importance of privacy and protecting democratic values. She expressed enthusiasm for the collaboration between the UK and its allies in creating innovative technologies that enable public institutions to combat financial crime and promote public health without compromising sensitive data confidentiality.
Recognizing the Winners
The prize challenges were designed and delivered through a collaborative, bilateral process. In the UK, the challenges were delivered by the Centre for Data Ethics and Innovation, part of the Department for Science, Innovation and Technology, and Innovate UK. In the US, the challenges were funded by the National Institute of Standards and Technology and the National Science Foundation, with partnership from the White House Office of Science and Technology Policy. Additional support was provided by the UK Information Commissioner’s Office, the UK Financial Conduct Authority, NHS England Transformation, the U.S. Financial Crimes Enforcement Network, Swift, and the University of Virginia Biocomplexity Institute.
US winners included Scarlet Pets (Rutgers University), PPML Huskies (University of Washington Tacoma, Delft University of Technology, University of Brasilia), and ILLIDAN Lab (Michigan State University, University of Calgary) in the Financial Crime Prevention track, while puffle (Carnegie Mellon University), MusCAT (Broad Institute, MIT, Harvard Business School, University of Texas Austin, University of Toronto), and ZS_RDE_AI (ZS Associates) were victorious in the Pandemic Response and Forecasting track.
UK winners were the joint winners of the University of Cambridge and STARLIT (Privitar, University College London, Cardiff University), followed by Faculty and Featurespace. Special Recognition was given to Visa Research, Faculty, Featurespace, Diagonal Works, Privitar, and the University of Liverpool.
Red Teams in the US included ETH SRI (ETH Zurich), Entmoot (independent researcher), and Blackbird Labs. In the UK, Trūata participated as a Red Team. White Paper Prizes were awarded to a variety of institutions, including MusCAT, IBM Research, Secret Computers (Inpher Inc), Corvus Research Limited, DeepMind and OpenMined, Diagonal Works, Faculty, Featurespace, GMV, Privately SA, STARLIT, University of Cambridge, and University of Liverpool. It should be noted that DeepMind and OpenMined chose not to accept any prize funds for this challenge.
The PETs prize challenge demonstrates the powerful potential of international collaboration in tackling global problems that often transcend borders. As U.S. National Science Foundation Director Sethuraman Panchanathan stated, “We are thrilled that the PETs prize challenge is galvanizing innovation and helping the research community close gaps and accelerate broader adoption of privacy-enhancing technologies.”
The success of these innovators in creating groundbreaking PETs solutions not only showcases the power of AI and machine learning but also signals a new era in data privacy that upholds democratic values while addressing critical global challenges. The joint Demo Day in London will mark another milestone in fostering deeper collaboration and knowledge sharing between the US and UK in the privacy-enhancing technology space.