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Project Information

Project Information

Challenges and New Approaches for Protecting Privacy in Federal Statistical Programs: A Workshop

Project Scope:

An ad hoc planning committee will organize a 1.5-day public workshop to discuss emerging practical issues concerning the protection of confidentiality of data subjects and their individually identifiable data while making information publicly available.  Federal statistical agencies are typically legally obligated to protect the privacy of data subjects' identities and sensitive attributes.  The rapid emergence of accessible digital data and powerful computational resources--while enormously beneficial for research and policy making--has made protecting privacy while ensuring analytic usefulness increasingly difficult.  Most of the tried and true methods employed to obfuscate data were designed for a different era and can no longer can be counted on.  

Researchers in computer science, economics, mathematics, and statistical science have been developing new methods for creating public use data products with provable guarantees of privacy protection.  Among these, differential privacy has emerged as an important approach because of its ability to provide provably formal privacy protection. To date, however, this research has not been widely transferred into practice, despite its theoretical advantages over typical approaches to privacy protection.  Workshop participants will have the opportunity to learn about the promises and limitations of using formal privacy methods for dissemination of data products. 

The planning committee will define the specific topics to be addressed, develop the agenda, and select and invite speakers and other participants. After the workshop, a proceedings of the presentations and discussions will be prepared by a designated rapporteur in accordance with institutional guidelines.

Status: Current


RSO: Harris-Kojetin, Brian


Committee on National Statistics


Behavioral and Social Sciences
Surveys and Statistics

Geographic Focus:

Committee Membership

Jerome P. Reiter - (Chair)
JEROME P. (JERRY) REITER is professor of statistical science at Duke University. Dr. Reiter participates in both applied and methodological research in statistics, and is most interested in applications involving social science and public policy. His methodological research focuses mainly on data confidentiality, missing data, and survey methodology. He is currently a member of CNSTAT and served on the CNSTAT Standing Committee for the American Opportunity Study-Phase 1; the CNSTAT Panel on Addressing Priority Technical Issues for the Next Decade of the American Community Survey--First Phase; the TRB Committee on the Long-Term Stewardship of Safety Data from the Second Strategic Highway Research Program; the CNSTAT-CPOP Panel on Collecting, Storing, Accessing, and Protecting Biological Specimens and Biodata in Social Surveys; and the Panel on the Census Bureau's Reengineered Survey of Income and Program Participation (SIPP). Dr. Reiter is a fellow of the American Statistical Association, an elected member of the International Statistical Institute, and deputy director of the Information Initiative at Duke, an institute dedicated to research and applications in the analysis of complex data. He has a B.S. degree in mathematics from Duke University, and an A.M. and Ph.D. in statistics from Harvard University.
Michael Hawes
MICHAEL HAWES is senior advisor for Data Access and Privacy at the U.S. Census Bureau. He is also currently the chair of the Federal Committee on Statistical Methodology’s Confidentiality and Data Access Committee (CDAC). Prior to joining the Census Bureau, Michael served as the director of Student Privacy at the U.S. Department of Education, where he was the senior policy official responsible for administering and enforcing federal student privacy laws. He is a certified Project Management Professional (PMP), and a Certified Information Privacy Professional/Government (CIPP/G). He has a B.A., summa cum laude, in political science and history from Duke University, and an M.A. in international relations from the University of Chicago.
Daniel Kifer
DANIEL KIFER is associate professor in the Department of Computer Science & Engineering and a member of the graduate faculty in the Social Data Analytics Department at The Pennsylvania State University. Dr. Kifer’s research interests include statistical privacy, machine learning, computational social science, and deep learning. He has won an influential paper award at the 2017 IEEE International Conference on Data Engineering and has developed some of the disclosure avoidance techniques used by the U.S. Census Bureau. His research has been supported by NSF, Google, Xerox, NVIDIA, Adobe, and Yahoo. Dr. Kifer has a B.A. degree from New York University, and a Ph.D. in computer science from Cornell University.
Aleksandra Korolova
ALEKSANDRA KOROLOVA is the WISE Gabilan assistant professor of computer science at the University of Southern California. Her research aims to develop and deploy algorithms and technologies that enable data-driven innovations while preserving privacy. Previously, Dr. Korolova was a research scientist at Google, and co-developr of RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response, a technology for crowdsourcing statistics from end-user client software, anonymously, with strong provacy guarantees. That work was a runner up for the 2015 Privacy Enhancing Tecnologies (PET) award. She was also a co-winner of the 2011 PET award for exposing provacy violations of microtargeted advertising. Dr. Korolova has a Ph.D. in computer science from Stanford University.
Alexandra Wood
ALEXANDRA WOOD is a fellow at the Berkman Klein Center for Internet and Society at Havard University and a senior researcher collaborating with the Harvard Privacy Tools Project. Her research involves exploring legal and regulatory frameworks for privacy and data protection in light of recent advances from fields such as computer science, social science, and law. She also contributes to the development of legal instruments to facilitate the sharing and use of research data while preserving privacy, transparency, and accountability. Previously, Ms. Wood served as a legal fellow assisting with the technology, telecommunications, and intellectual property portfolio of U.S. Senator Barbara Boxer. As a law student, she worked with the Center for Democracy & Technology and the Electronic Privacy Information Center on privacy projects addressing emerging electronic surveillance, facial recognition, and mobile payments technologies. She holds a J.D. from George Washington University Law School, an M.P.P. from the University of Southern California, and a B.A. degree in economics from Reed College.



National Academy of Sciences Building
2101 Constitution Ave NW, Washington, DC 20418
Event Type :  

Description :   

For decades, federal statistical agencies have striven to balance the legal and ethical obligations to protect the confidentiality of data subjects with the need to provide informative statistics and access to data for secondary analysis. In recent years, balancing these objectives has become increasingly difficult. The digital revolution has seen an explosion in the growth of available data, both from public and private sources, which ill-intentioned actors could use to compromise confidentiality protections.

This workshop will discuss new approaches to protecting data confidentiality, with a particular focus on methods that offer formal guarantees of privacy protection, like differential privacy. Discussions will cover the policy and implementation issues from both provider and user perspectives, including the promises and limitations of using formal privacy methods.

Videos from the workshop can be found here

If you would like to attend the sessions of this event that are open to the public or need more information please contact

Contact Name:  Jillian Kaufman
Contact Email:
Contact Phone:  (202) 334-3465

Supporting File(s)
Workshop Flyer

Is it a Closed Session Event?

Publication(s) resulting from the event:




No data present.