U.S. PETs Prize Challenge: Phase 1 Hosted By NIST and NSF
PETs Prize Challenge: Advancing Privacy-Preserving Federated Learning
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 organizations to analyze 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.
The goal of this prize challenge is to mature federated learning approaches and build trust in their adoption. The challenge organizers hope to accelerate the development of efficient privacy-preserving federated learning solutions that leverage a combination of input and output privacy techniques to:
- Drive innovation in the technological development and application of novel privacy-enhancing technologies
- Deliver strong privacy guarantees against a set of common threats and privacy attacks
- Generate effective models to accomplish a set of predictive or analytical tasks that support the use cases
You will develop solutions that enhance privacy protections across the lifecycle of federated learning. This challenge offers two different data use cases—financial crime prevention (Track A) and pandemic response and forecasting (Track B). You may develop solutions directed at either one or both tracks. If you choose to develop and submit a generalizable solution that applies to both Track A and B, you can also qualify for additional prizes dedicated to Generalizable Solutions.
Data Track A – Financial crime prevention – The United Nations estimates that up to $2 trillion of cross-border money laundering takes place each year, financing organized crime and undermining economic prosperity. Financial institutions such as banks and credit agencies, along with organizations that process transactions between institutions, such as the SWIFT global financial messaging provider, must protect personal and financial data, while also trying to report and deter illicit financial activities. Using synthetic datasets provided by SWIFT, you will design and later develop innovative privacy-preserving federated learning solutions that facilitate cross-institution and cross-border anomaly detection to combat financial crime. This use case features both vertical and horizontal data partitioning. Check out the Financial Crime overview page for more information.
Data Track B – Pandemic response and forecasting – As we continue to deal with COVID-19, it has become apparent that better ways to harness the power of data through analytics are critical for preparing for and responding to public health crises. Federated learning approaches could allow for responsible use of sensitive data to develop cross-organization and cross-border data analysis that would result in more robust forecasting and pandemic response capabilities. Using synthetic population datasets, you will design and later develop privacy-preserving federated learning solutions that can predict an individual’s risk for infection. This use case features horizontal data partitioning. Check out the Pandemic Forecasting overview page for more information.
Generalizable Solutions – Cross-organization, cross-border use cases are certainly not limited to the financial or public health domains. Developing out-of-the-box generalized models that can be adapted for use with specific data or problem sets has great potential to advance the adoption and widespread use of privacy-preserving federated learning for public- and private-sector organizations in multiple sectors. To demonstrate generalizability, you may develop a solution using both the Track A and Track B datasets to be eligible for additional awards dedicated to generalizable solutions.
More information on how to get started can be found in the Problem Description.
At the time of entry, the Official Representative (individual or team lead, in the case of a group project) must be age 18 or older and a U.S. citizen or permanent resident of the United States or its territories. In the case of a private entity, the business shall be incorporated in and maintain a place of business in the United States or its territories.
Participants in this Challenge, whether they are individuals, entities, or team members, are prohibited from participating in the U.K. prize challenge, and participants in the U.K. prize challenge are likewise prohibited from participating in this Challenge. Individuals, entities, or team members who are found to have entered both the U.S. and the U.K. challenges will be disqualified from participating in either. If you wish to instead participate in the U.K. competition, please register here.
For more details, please refer to the official rules.
Phase 1 Timeline and Prizes
This is the first in a series of challenge phases with a total prize pool of $800,000! Each phase in the PETs Prize Challenge invites participants to further test and apply their innovations in privacy-preserving federated learning. Phase 1 is for Blue Teams to begin development on their solutions—more details are provided on blue teams and red teams below in the Full Challenge Timeline and Prizes section.
Note that your team must register and meet all requirements for Phase 1 in order to continue to be eligible to participate in Phase 2.
Phase 1 Key Dates
|Launch & Blue Team Registration Opened||July 20, 2022|
|Abstracts Due & Blue Team Registration Closed||September 4, 2022 at 11:59:59 PM UTC|
|Concept Papers Due||September 19, 2022 at 11:59:59 PM UTC|
|Winners Announced||November 10, 2022|
Phase 1 Prizes
Concept Paper Deadline:
Sept. 19, 2022, 11:59 p.m. UTC
Full Challenge Timeline and Prizes
There are three main phases in the challenge with two types of participants based on a red team/blue team approach. Blue Teams develop privacy-preserving solutions, while Red Teams act as adversaries to test those solutions.
- Phase 1: Concept Development (Jul–Sept 2022): Blue Teams propose privacy-preserving federated learning solution concepts.
- Phase 2: Solution Development (Oct 2022–Jan 2023): Blue Teams develop working prototypes of their solutions.
- Phase 3: Red Teaming (Nov 2022–Feb 2023): Red Teams prepare and test privacy attacks on top blue team solutions from Phase 2.
Phase Details and Prizes
Phase 1: Concept Paper
July 20–September 19, 2022 (YOU ARE HERE)
Participants will produce an abstract and technical concept paper laying out their proposed solution. Concept papers will be evaluated by a panel of judges across a set of weighted criteria. Participants will be eligible to win prizes awarded to the top technical papers, ranked by points awarded. Participants must complete a paper in Phase 1 in order to be eligible to compete in Phase 2.
Open to Blue Team participants.
Data Track A: Financial Crime Prevention
Data Track B: Pandemic Response and Forecasting
Phase 2: Solution Development
October 5, 2022–January 24, 2023
Put your papers into action! Registered teams from Phase 1 will develop working prototypes and submit them to a remote execution environment for federated training and evaluation. These solutions are expected to be functional, i.e., capable of training a model and predicting against the evaluation data set with measurement of relevant performance and accuracy metrics. Solutions will be evaluated by a panel of judges across a set of weighted criteria. The top solutions, ranked by points awarded, will have their final rankings determined by incorporating red team evaluation from the red teams from Phase 3.
Teams can qualify for one or multiple of Data Track A: Financial Crime Prevention, Data Track B: Pandemic Response and Forecasting, and Generalized Solutions prize categories depending on how their solutions address the two privacy-preserving federated learning tasks in the challenge.
A separate Special Recognition prize pool is set aside to award up to five solutions that do not win prizes from the three main prize categories but demonstrate excellence in specific areas of privacy innovation: novelty, advancement in a specific privacy technology, usability, and efficiency.
Open to Blue Team participants.
Phase 3: Red Teaming
November 10, 2022–February 28, 2023
Privacy researchers are invited to form red teams to put the privacy claims of Phase 2's blue team finalists to the test. The red teams will prepare and test privacy attacks on the Phase 2 finalist solutions, which will be incorporated into the final Phase 2 rankings. Top red teams will be evaluated for success and rigor and will be awarded prizes for their performance during this phase.
Open to Red Team participants.
Submissions due April 7, 2023
Up to 5 of the final blue team winners from Phase 2 will be invited to release their solutions as open-source software. Each verified participating blue team will be awarded an Open Source prize of $10,000.
Open to top Blue Team winners from Phase 2.
How to compete (Phase 1)
- Click the "Compete!" button in the sidebar to enroll in the competition.
- Click on "Team" if you need to create or join a team with other participants. To find other participants to form a team with, check out our community forum or the cross-U.S.–U.K. public Slack channel. You can request access to the Slack channel here.
- Get familiar with the problem through the data overview and problem description pages.
- Summarize your modeling and privacy preserving techniques in an abstract, making sure to follow the instructions on the problem description page. Submit it by clicking on "Abstract Submission" in the sidebar and filling out the form. Partway there!
- Dive into the details of your modeling and privacy preserving techniques in a concept paper, making sure to follow the instructions on the problem description page. Submit it by clicking on "Concept Paper Submission" in the sidebar and filling out the form. You're in!
Note that registration closes on September 4, 2022 at the same time that abstracts are due. Your team must register by this deadline to participate in Phase 1 or Phase 2 of the challenge.