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Census Bureau Awards Cooperative Agreements to Georgetown University and Purdue University

Tue Oct 18 2016
John Abowd, associate director, Research and Methodology Directorate
Component ID: #ti598162787

Today, the U.S. Census Bureau awarded two cooperative agreements to research teams at Georgetown University and Purdue University. These teams of university-based researchers are at the forefront of the emerging field of privacy-preserving data analysis, and their efforts will assist the Census Bureau in ensuring we continue to be a leader in protecting confidential information.

Component ID: #ti2043132679

The Georgetown University project will help develop methods for publishing data that satisfy both formal mathematical privacy requirements and legal standards for privacy protection. Their research, combined with ongoing research at the Census Bureau, will provide improvements to existing methods that protect privacy by avoiding the release of any information that would identify an individual or business in public statistics.

The projects complement new initiatives within the Census Bureau to strengthen our disclosure avoidance methods, especially as they apply to the detailed publications that result from our flagship products: the 2020 Census, the American Community Survey, and the 2017 Economic Census.

The team from Georgetown University, led by Kobbi Nissim, includes two of the computer scientists who originally developed the theory of differential privacy — the first privacy-preserving data analysis model — as well as leading researchers from Harvard University who specialize in cryptography and information law. Their work for the Census Bureau will help improve the way we understand and implement our statutory mandate to protect the confidentiality of all respondent information in the Big Data era.

The Purdue University project will investigate methods to improve the usefulness of anonymized data by studying systems where automated techniques perform many of the tasks currently performed directly by data analysts preparing the publication products. These private automated techniques have the potential to produce high-quality publishable data without compromising the privacy of the respondents even inside the Census Bureau. This research is complementary to our ongoing research on methods that strengthen traditional disclosure avoidance techniques.

Chris Clifton, a computer scientist with an extensive research record in data anonymization leads the team from Purdue University. He is a past program director in the National Science Foundation’s Computing and Information Science Directorate. Their research is expected to help the Census Bureau better understand how to preserve the suitability of our data products for their many uses once we adopt modern privacy-preserving anonymization methods better adapted to the Big Data era.

Both awards are three-year collaborative efforts that will provide us with the time to research, test and further refine innovation methods to enhance our assurance of the protection of confidentiality mandated by U.S.C. Title 13.

The Census Bureau’s mission is to serve as the leading source of quality data about the nation’s people and economy. We honor privacy, protect confidentiality, share our expertise globally, and conduct our work openly. These new cooperative agreements provide complementary approaches to innovative methods and procedures for executing the dual statutory mandates in Title 13 U.S.C. — collect data in order to publish statistics and maintain the confidentiality of respondent information.

Moving forward, the Census Bureau intends to use Cooperative Agreement Authority to enter into partnership with leading experts in order to produce innovative work and to ensure that we remain the leading source of quality data about the nation’s people and economy. We will use this important tool to engage with leading experts in academia, researchers and nonprofit agencies. Our goal is to find the best sources of data, the best methods to analyze these data, and the best tools to provide data to the public.

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