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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

Videos and Presentations from the Workshop

Jerry Reiter (committee chair), Duke University
Brian Harris-Kojetin, CNSTAT Director

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

Discussion: Risks of Existing Statistical
Disclosure Limitation Methods


NIST's Role in Privacy Protection
Mary Theofanos, National Institute of Standards and Technology

Overview of the Needs for Federal Statistics
Michael Hawes, U.S. Bureau of the Census

Darius Singpurwalla, National Center for Science and Engineering Statistics 

NCHS Disclosure Concerns In Our Modern World
Donna Miller, National Center for Health Statistics

Discussion: Insights from Federal Agencies about Privacy Protection Needs

Tutorial on Differential Privacy
Kobbi Nissim, Georgetown University 
Alexandra Wood, Harvard University

Discussion about Differential Privacy 

Ashwin Machanavajjhala, Duke University
Privately Releasing Statistics

Ilya Mironov, Google
Deep Learning with Differential Privacy: Two Approaches

Differential Privacy for Social Science Research
Salil Vadhan, Harvard University

Panel Disscussion: Current Technical Capabilities in Formal Privacy

Nancy Potok, U.S. Office of Management and Budget

John Abowd, U.S. Bureau of the Census


Privacy Protection at Statistics of Income, IRS
Barry Johnson, Statistics of Income Division of the Internal Revenue Service 

Panel Discussion: Policy Issues and Practical Experiences with Formal Privacy

Day 2 Welcome and Summary of Day 1
Jerry Reiter, Duke University


Implications of Formal Privacy for Data Users
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 to Protect Privacy 
Mike Davern, NORC


A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples
John Friedman, Brown University, NBER


Panel Discussion: Implications of Formal Privacy for Data Users


What Can Agencies Do Right Now to Improve Privacy Protections?
Tom Krenzke, Westat

What Could be Done?
Lars Vilhuber, Cornell University

Workshop Summary
Jerry Reiter, Duke University


Q&A Session: Looking Forward to the Near Future 


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 (Jenny) 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 

Open Discussion


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