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The National Academies of Sciences, Engineering and Medicine
Committee on National Statistics
Division of Behavioral and Social Sciences and Education
Challenges and New Approaches for Protecting Privacy in Federal Statistical Programs
Project 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.

Workshop | June 6-7, 2019
National Academy of Sciences Building- NAS Lecture Room
2101 Constitution Ave., NW
Washington, DC 

June 6: 8:45 AM - 4:00 PM
June 7: 9:00 AM - 2:20 PM

Workshop Agenda

Workshop Flyer


Session 1- Risks of existing statistical disclosure limitation methods
Reconstruction of person level data from data presented in multiple tables- Philip Leclerc, U.S. Bureau of the Census
De-Anonymizing the Australian Medicare Data Release- Ben Rubinstein, University of Melbourne, Australia

Session 2- Insights from federal agencies about privacy protection needs
NIST's Role in Privacy Protection- Mary Theofanos, National Institute of Standards and Technology
NCHS Disclosure Concerns In Our Modern World- Donna Miller, National Center for Health Statistics

Session 3
Tutorial on differential privacy- Kobbi Nissim, Georgetown University; Alexandra Wood, Harvard University

Session 4- Panel discussion: Current technical capabilities in formal privacy
Privately Releasing Statistics- Ashwin Machanavajjhala, Duke University
Deep Learning with Differential Privacy: Two Approaches- Ilya Mironov, Google
Differential Privacy for Social Science Research- Salil Vadhan, Harvard University

Session 5- Panel discussion: Policy issues and practical experiences with formal privacy
Privacy Protection at Statistics of Income, IRS- Barry Johnson, Statistics of Income Division of the Internal Revenue Service

Session 6- Panel discussion: Implications of formal privacy for data users
Implications- Julia Lane, New York University
2020 Census Data Products The Data Users' Perspective- Joe Salvo, New York City Department of City Planning
Data User Perspectives on Potential Changes to Data Products too Protect Privacy- Mike Davern, NORC
A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples- John Friedman, Brown University, NBER

Session 7- Panel discussion: Looking forward to the near future
What Can Agencies Do Right Now to Improve Privacy Protections?- Tom Krenzke, Westat
What could be done?- Lars Vilhuber, Cornell University

Session 8- Speed session with flash presentations
Data Privacy and the USDA Forest Service’s Forest Inventory and Analysis Program Surveys- Brett Butler, USDA Forest Service
Full-Rank Sufficient Statistics for Limiting Disclosure of Cross- Classifications- Yves Thibaudeau, U.S. Bureau of the Census
Changing Farm Structure and its Potential Impact on Ag-Census Disclosure Analysis- Bayazid Sarkar, USDA NASS
Achieving Differential Privacy Using a Two-Tailed Geometric "Bottom-Up" Mechanism- Stephen Clark, U.S. Bureau of the Census
Implications of Differential Privacy for America's Children- Bill O’Hare, O’Hare Data and Demographics
Research on Using Synthetic Microdata to Protect Economic Data: Utility and Privacy Protection- Katherine Thompson, U.S. Bureau of the Census
Synthetic Data Quality Metrics: Relative vs. Absolute- Christine Task, Knexus Research
Respondents' Understanding of Disclosure Avoidance- Aleia Yvonne Clark Fobia, U.S. Bureau of the Census
Bayesian Pseudo Posterior Inference for Data Privacy Protection- Terrance Savitsky, Bureau of Labor Statistics
CBAMS: Public Microdata with Randomized Response- Caleb Floyd, U.S. Bureau of the Census

Staff Information

Nancy J. Kirkendall, Project Director
Jillian Kaufman, Senior Program Assistant 

National Academy of Sciences
Committee on National Statistics


Committee Members
Jerome P. Reiter (Chair), Duke University
Daniel Kifer, Penn State University
Aleksandra Korolova, University of Southern California
Alexandra Wood, Harvard University
Michael Hawes, U.S. Bureau of the Census


For more information, please contact: 

Jillian Kaufman

Mailing Address
Keck Center W1116
500 Fifth Street, NW 
Washington, DC 20001







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