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Topics
Data & Maps
Surveys & Programs
Resource Library
Understanding Differential Privacy
Understanding Differential Privacy
Census confidentiality protections—what we call “disclosure avoidance”—have evolved over time to keep pace with emerging threats. Since the 1990 Census we’ve added “noise”—or variations from the actual count—to the collected data. For 2020 Census data we’re applying noise using a newer protection framework based on “differential privacy.” Learn more here about why and how we’re modernizing our protections and how you can engage in the process.
For an overview, read this brief: Why the Census Bureau Chose Differential Privacy
On this page:
Why We’re Modernizing Census Disclosure Avoidance
Reconstruction / Re-identification research
Working Paper
The 2010 Census Confidentiality Protections Failed, Here’s How And Why
We perfectly reconstruct 2010 Census records for 97 million people, correctly infering race and ethnicity for 3.4 million vulnerable population uniques.
Webinar
Simulated Reconstruction Re-identification Attack on the 2010 Census
This webinar examines the simulated re-identification attack that the Census Bureau performed on the published 2010 Census data.
Blogs
Random Samplings Blog
Census Bureau Adopts Cutting Edge Privacy Protections for 2020 Census
The Census Bureau takes seriously its legal and professional obligation to safeguard the information it gathers from the public.
Director's Blog
The Balancing Act of Producing Accurate and Confidential Statistics
Several months ago, Dr. John Abowd, our chief scientist at the Census Bureau, blogged about the need for disclosure avoidance measures and the considerations that went into our decision to implement differential privacy.
Research Matters Blog
Protecting the Confidentiality of America's Statistics: Ensuring Confidentiality and Fitness-for-Use
In my last blog, I described the Census Bureau’s work to modernize how we protect respondent data in the statistics we publish.
Research Matters Blog
Protecting the Confidentiality of America's Statistics: Adopting Modern Disclosure Avoidance Methods at the Census Bureau
The U.S. Census Bureau’s commitment to data stewardship—protecting respondent privacy and confidentiality at every stage of the data lifecyle—is grounded in law that is straightforward, robust, and strong.
Research Matters Blog
Challenges Facing the Disclosure Review Board
Scientists from the Census Bureau describe the importance of research in survey design, methodology, modeling, record linkage, confidentiality protection, etc.
How Census Disclosure Avoidance Methods Have Evolved over Time
How Differential Privacy Works: The Basics
Webinars
Webinar
Differential Privacy 101
This introductory webinar will explain the Census Bureau’s decision to modernize the methods we use to protect respondent confidentiality in data we release.
Webinar
Research into Alternatives to Differential Privacy
Census Bureau researchers present preliminary findings from experiments designed to illustrate techniques used to protect privacy.
Fact Sheets
Fact Sheet
Understanding Disclosure Avoidance-Related Variability 2020 Census
This research is part of comprehensive efforts after each census to better understand sources of variability—both their scope and potential impacts.
Fact Sheet
Comparing Differential Privacy With Older Disclosure Avoidance Methods
Differential privacy, first developed in 2006, is a framework for measuring the precise disclosure risk associated with each release of confidential data.
Fact Sheet
Protecting the Confidentiality of 2020 Census Redistricting Data
By law, we must ensure that we don’t release information that could identify your information in the statistics we publish.
Fact Sheet
Differential Privacy: A Modern Approach to Protecting Your Confidentiality
This fact sheet describes what differential privacy is and how it provides precise protection against digital-age threats.
Blogs
Director's Blog
Redistricting Data: What to Expect and When
Since releasing the apportionment results in April, we’ve had several teams working hard on the next set of 2020 Census data — the redistricting data.
Random Samplings Blog
Modernizing Privacy Protections for the 2020 Census: Next Steps
The world has evolved quite a bit since 1990, the last time we changed the way we protect individual responses in published census statistics.
Technical Background Information
Webinars
Webinar
Differential Privacy 201 and the TopDown Algorithm
This webinar provides an examination of how the Census Bureau is using framework of differential privacy to safeguard respondent data for the 2020 Census.
Webinar
Research into Alternatives to Differential Privacy
Census Bureau researchers present preliminary findings from experiments designed to illustrate techniques used to protect privacy.
Publication
Consistency of Data Products and Formal Privacy Methods for the 2020 Census
JASON was asked to study the impact of these privacy mechanisms on consistency
for two data products produced from the 2020 census data.
Publication
Assessment of 2020 Census Data Quality Processes
This JASON report assesses data quality processes, procedures, and plans and provides insights and recommendations on areas for improvement.
Papers
- Abowd, J.M., Benedetto, G.L., et al. (2020) The Modernization of Statistical Disclosure Limitation at the U.S. Census Bureau. Census Bureau Working Paper.
- Dinur, I. and Nissim, K. (2003) “Revealing Information while Preserving Privacy” PODS, June 9-12, San Diego, CA. https://doi.org/10.1145/773153.773173.
- “U.S. Census Bureau Reidentification Studies,” available at https://www.census.gov/library/working-papers/2019/adrm/2019-04-ReidentificationStudies.html.
- Abowd, J. M., & Schmutte, I. M. (2019). An economic analysis of privacy protection and statistical accuracy as social choices. American Economic Review, 109(1), 171-202.
- Garfinkel, S., Abowd, J. M., & Martindale, C. (2018). Understanding Database Reconstruction Attacks on Public Data: These attacks on statistical databases are no longer a theoretical danger. Queue, 16(5), 28-53.
- Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006, March). Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference (pp. 265-284). Springer, Berlin, Heidelberg.
- Haney, S., Sexton, W., Machanavajjhala, A., Hay, M., Miklau, G., Tumult Labs, science@tmlt.io (2021) Differentially Private Algorithms for 2020 Census Detailed DHC Race & Ethnicity.
Page Last Revised - February 29, 2024
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