Innovate UK, part of UK Research and Innovation, will work with the Centre for Data Ethics and Innovation (part of the Department for Digital, Culture, Media and Sport) to run a Privacy Enhancing Technologies Challenge. This is part of an aligned programme with the US National Science Foundation, the White House Office of Science and Technology Policy, and the National Institute of Standards and Technology.
The aim of this Challenge is to accelerate the development and adoption of privacy-preserving federated learning approaches, and build trust in their adoption.
To achieve this, participants are asked to develop approaches that:
The challenge is split into 3 phases:
Up to £700,000 is available in funding across the three phases of the competition.
Applicants successful in Phase 1 and invited to Phase 2 will be able to apply for up to £50,000 to develop their solutions in Phase 2. Up to 10 awards of £10,000 will also be given to the highest scoring projects from phase 1. The £10,000 can only be used to support with the growth of your business and the costs associated with this must be evidenced.
This application process represents the UK side of the challenge. US applicants must apply to the US competition. Each organisation can only apply once into either the UK or US competition. This includes if your organisation is linked in anyway.
Eligibility
If successful and invited, your phase 2 project must:
You can only claim for eligible project costs for your phase 2 projects.
Under current restrictions, this competition will not fund any procurement, commercial, business development or supply chain activity with any Russian entity as lead, partner or subcontractor. This includes any goods or services originating from a Russian source.
Your organisation must be UK registered:
Our funding rules will give you more information on organisation types.
Subcontractors are allowed in this competition.
The aim of this competition is to develop innovative privacy-preserving solutions that address one or both of the specific challenge use cases in financial crime or public health.
You must develop privacy-preserving federated learning solutions that:
You must determine the set of privacy technologies used in your solutions.
For example:
We encourage projects that:
We want to fund a variety of projects across different technologies, markets, technological maturities and research categories. We call this a portfolio approach.
Specific themes
Your project must address one or both of the pre-defined, high-impact use cases.
You must specify the use case in your application, and can submit technical solutions for both use cases. The technical evaluation of these will be treated separately.
A full technical briefing is attached to each use case, providing details of the datasets, analytical tasks, and evaluation criteria that will be used during the challenge. The briefings will also outline the requirements for what should be included in the white paper.
Use case 1: Financial Crime Prevention
This use case is focused on enhancing cross-organisation and cross-border data access, supporting efforts to combat money laundering and other financial crime.
You will utilise synthetic datasets representing data held by the SWIFT payments network and datasets held by partner banks.
This is a high-impact use case for novel privacy enhancing technologies. Successful solutions will allow for effective detection of illegal financial activity while addressing the challenges arising between enabling sufficient access to data and successfully limiting the identifiability of innocent individuals and possibility of inference of their sensitive information from that data.
The scale of the problem is vast: the UN estimates that US$800-2000bn is laundered each year, representing 2-5% of global GDP.
A full technical briefing for this use case can be found here.
Use case 2: Pandemic response and forecasting
This use case is focused on enabling privacy-preserving access to health and mobility data in order to improve forecasting related to public health emergencies, and there by bolster response capabilities for future emergencies, including pandemics.
You will utilise synthetic datasets representing data held by the University of Virginia. This use case is an opportunity to prepare for future epidemics and public health emergencies.
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