U.K. PETs Prize Challenge
Privacy-enhancing technologies (PETs) have the potential to unlock more trustworthy innovation in data analysis and machine learning. Federated learning is one such technology that enables organisations to analyse sensitive data while providing improved privacy protections. These technologies could advance innovation and collaboration in new fields and help harness the power of data to tackle some of our most pressing societal challenges.
That’s why the U.S. and U.K. governments are partnering to deliver a set of prize challenges to unleash the potential of these democracy-affirming technologies to make a positive impact. In particular, this challenge will tackle two critical problems via separate data tracks: Data Track A will help with the identification of financial crime, while Data Track B will bolster pandemic responses.
By entering the prize challenges, innovators will have the opportunity to compete for cash prizes and engage with regulators and government agencies. Announced at the Summit for Democracy in December 2021, the prize challenges are a product of a collaboration between multiple government departments and agencies on both sides of the Atlantic. Winning solutions will have the opportunity to be profiled at the second Summit for Democracy, to be convened by President Joe Biden, in early 2023.
You are on the home page for the U.K. side of the challenge. You can find the U.S. challenge here.
Phase 2: Solution Development
Phase 2 - Financial Crime
Help unlock the potential of privacy-enhancing technologies (PETs) to combat global societal challenges. Develop efficient, accurate, and extensible federated learning solutions with strong privacy guarantees for individuals in the data.
Phase 2 - Pandemic Forecasting
Welcome to Phase 2 for prescreened participants! Help unlock the potential of privacy-enhancing technologies (PETs) to combat global societal challenges. Develop efficient, accurate, and extensible federated learning solutions with strong privacy guarantees for individuals in the data.