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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
Frequently Asked Questions
Reconstruction / Re-identification research
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Working PaperThe 2010 Census Confidentiality Protections Failed, Here’s How And WhyWe perfectly reconstruct 2010 Census records for 97 million people, correctly infering race and ethnicity for 3.4 million vulnerable population uniques.
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WebinarSimulated Reconstruction Re-identification Attack on the 2010 CensusThis webinar examines the simulated re-identification attack that the Census Bureau performed on the published 2010 Census data.
Blogs
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Random Samplings BlogCensus Bureau Adopts Cutting Edge Privacy Protections for 2020 CensusThe Census Bureau takes seriously its legal and professional obligation to safeguard the information it gathers from the public.
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Director's BlogThe Balancing Act of Producing Accurate and Confidential StatisticsSeveral 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.
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Research Matters BlogProtecting the Confidentiality of America's Statistics: Ensuring Confidentiality and Fitness-for-UseIn my last blog, I described the Census Bureau’s work to modernize how we protect respondent data in the statistics we publish.
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Research Matters BlogProtecting the Confidentiality of America's Statistics: Adopting Modern Disclosure Avoidance Methods at the Census BureauThe 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.
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Research Matters BlogChallenges Facing the Disclosure Review BoardScientists 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
Infographic
Papers
Developing the new Disclosure Avoidance System
Frequently Asked Questions
How Differential Privacy Works: The Basics
Handbooks
Videos
Webinars
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WebinarDifferential Privacy 101This introductory webinar will explain the Census Bureau’s decision to modernize the methods we use to protect respondent confidentiality in data we release.
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WebinarResearch into Alternatives to Differential PrivacyCensus Bureau researchers present preliminary findings from experiments designed to illustrate techniques used to protect privacy.
Fact Sheets
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Fact SheetUnderstanding Disclosure Avoidance-Related Variability 2020 CensusJanuary 28, 2022This research is part of comprehensive efforts after each census to better understand sources of variability—both their scope and potential impacts.
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Fact SheetComparing Differential Privacy With Older Disclosure Avoidance MethodsAugust 12, 2021Differential privacy, first developed in 2006, is a framework for measuring the precise disclosure risk associated with each release of confidential data.
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Fact SheetProtecting the Confidentiality of 2020 Census Redistricting DataAugust 12, 2021By law, we must ensure that we don’t release information that could identify your information in the statistics we publish.
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Fact SheetDifferential Privacy: A Modern Approach to Protecting Your ConfidentialityMay 08, 2023This fact sheet describes what differential privacy is and how it provides precise protection against digital-age threats.
Blogs
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Director's BlogRedistricting Data: What to Expect and WhenSince releasing the apportionment results in April, we’ve had several teams working hard on the next set of 2020 Census data — the redistricting data.
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Random Samplings BlogModernizing Privacy Protections for the 2020 Census: Next StepsThe world has evolved quite a bit since 1990, the last time we changed the way we protect individual responses in published census statistics.
Frequently Asked Questions
Technical Background Information
Webinars
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WebinarDifferential Privacy 201 and the TopDown AlgorithmThis webinar provides an examination of how the Census Bureau is using framework of differential privacy to safeguard respondent data for the 2020 Census.
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WebinarResearch into Alternatives to Differential PrivacyCensus Bureau researchers present preliminary findings from experiments designed to illustrate techniques used to protect privacy.
Misc. Background Resources
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PublicationConsistency of Data Products and Formal Privacy Methods for the 2020 CensusJASON was asked to study the impact of these privacy mechanisms on consistency for two data products produced from the 2020 census data.
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PublicationAssessment of 2020 Census Data Quality ProcessesThis 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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