The National Institute of Standards and Technology (NIST) has launched the Unlinkable Data Challenge with a $50k grand prize!![NIST seeking proposals for industry-guided partnerships pursuing post-CMOS technology [image courtesy NIST].](https://cccblog.org/wp-content/uploads/2012/01/NIST_Logo-300x102.jpeg){kind=logo}
The challenge aims to advance approaches to differential privacy, a term introduced by Dwork, McSherry, Nissim, and Smith in 2006, which refers to the privacy loss that occurs when an individual’s information is used in the manufacture of a large dataset. NIST is calling for concept papers that propose “a mechanism to enable the protection of personally identifiable information while maintaining a dataset’s utility for analysis.”
How to Participate:
The Unlinkable Data Challenge is a multi-stage Challenge. This first stage of the Challenge is intended to source detailed concepts for new approaches, inform the final design in the two subsequent stages, and provide recommendations for matching stage 1 competitors into teams for subsequent stages. Teams will predict and justify where their algorithm fails with respect to the utility-privacy frontier curve.
In this stage, competitors are asked to propose how to de-identify a dataset using less than the available privacy budget, while also maintaining the dataset’s utility for analysis. For example, the de-identified data, when put through the same analysis pipeline as the original dataset, produces comparable results (i.e. similar coefficients in a linear regression model, or a classifier that produces similar predictions on sub-samples of the data).
This stage of the Challenge seeks Conceptual Solutions that describe how to use and/or combine methods in differential privacy to mitigate privacy loss when publicly releasing datasets in a variety of industries such as public safety, law enforcement, healthcare/biomedical research, education, and finance. We are limiting the scope to addressing research questions and methodologies that require regression, classification, and clustering analysis on datasets that contain numerical, geo-spatial, and categorical data.
To compete in this stage, we are asking that you propose a new algorithm utilizing existing or new randomized mechanisms with a justification of how this will optimize privacy and utility across different analysis types. We are also asking you to propose a dataset that you believe would make a good use case for your proposed algorithm, and provide a means of comparing your algorithm and other algorithms.
All submissions must be made using the submission form provided on HeroX website. Submissions will be judged using the listed criteria and scoring scheme. Challenge Sponsor has the right to make updates and/or make any changes at any time during the Challenge. (see official rules)
Teams that participate in the HeroX challenge, as well as newly formed teams that did not participate, can proceed to a leader-board-driven competition on Topcoder, the Algorithm Competition #1. It is anticipated that Competition #1 will be followed by iterating improvements in the Algorithm Sprint, and finish with a final penultimate Challenge to further boost performance in the Algorithm Competition #2. Where a competitor’s algorithm falls with respect to the utility-privacy frontier curve will determine who wins subsequent Topcoder Competitions. The final review and decision of the judge will be announced in accordance of the rules on this site.
Cynthia Dwork
Former CCC Council Member Cynthia Dwork, who helped coin the term differential privacy, has contributed frequently to the CCC’s work on privacy and fairness as part of the task force on the topic. To learn more about her work and differential privacy check out Microsoft Research’s lecture series featuring Dwork on Youtube.
Recent CCC activities in this space include the white papers Privacy-Preserving Data Analysis for the Federal Statistical Agencies (co-authored by Dwork); Big Data, Data Science, and Civil Rights; and Towards a Privacy Research Roadmap for the Computing Community; as well a March, 2018 workshop on Fair Representations and Fair Interactive Learning and the 2015-2016 Privacy by Design workshop series.
Submissions for the NIST Unlinkable Data Challenge are open on the HeroX website until July 26th. To learn more about the challenge and to explore the judging criteria visit the Unlinkable Data Challenge page on challenge.gov.
