The U.S. Census Bureau conducts the decennial censuses under Title 13 of the U. S. Code with the Section 9 mandate to not 'use the information furnished under the provisions of this title for any purpose other than the statistical purposes for which it is supplied; or make any publication whereby the data furnished by any particular establishment or individual under this title can be identified; or permit anyone other than the sworn officers and employees of the Department or bureau or agency thereof to examine the individual reports (13 U.S.C. ' 9 (2007)).' The Census Bureau applies disclosure avoidance techniques to its publicly released statistical products in order to protect the confidentiality of its respondents and their data.
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A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census: Full Technical Report
December 2023
Authors:
Lars Vilhuber,
John M. Abowd,
Ethan Lewis,
Nathan Goldschlag,
Robert Ashmead,
Daniel Kifer,
Philip Leclerc,
Rolando A. RodrÃguez,
Tamara Adams,
David Darais,
Sourya Dey,
Simson L. Garfinkel,
Scott Moore,
Ramy N. Tadros
Working Paper Number:
CES-23-63R
For the last half-century, it has been a common and accepted practice for statistical agencies, including the United States Census Bureau, to adopt different strategies to protect the confidentiality of aggregate tabular data products from those used to protect the individual records contained in publicly released microdata products. This strategy was premised on the assumption that the aggregation used to generate tabular data products made the resulting statistics inherently less disclosive than the microdata from which they were tabulated. Consistent with this common assumption, the 2010 Census of Population and Housing in the U.S. used different disclosure limitation rules for its tabular and microdata publications. This paper demonstrates that, in the context of disclosure limitation for the 2010 Census, the assumption that tabular data are inherently less disclosive than their underlying microdata is fundamentally flawed. The 2010 Census published more than 150 billion aggregate statistics in 180 table sets. Most of these tables were published at the most detailed geographic level'individual census blocks, which can have populations as small as one person. Using only 34 of the published table sets, we reconstructed microdata records including five variables (census block, sex, age, race, and ethnicity) from the confidential 2010 Census person records. Using only published data, an attacker using our methods can verify that all records in 70% of all census blocks (97 million people) are perfectly reconstructed. We further confirm, through reidentification studies, that an attacker can, within census blocks with perfect reconstruction accuracy, correctly infer the actual census response on race and ethnicity for 3.4 million vulnerable population uniques (persons with race and ethnicity different from the modal person on the census block) with 95% accuracy. Having shown the vulnerabilities inherent to the disclosure limitation methods used for the 2010 Census, we proceed to demonstrate that the more robust disclosure limitation framework used for the 2020 Census publications defends against attacks that are based on reconstruction. Finally, we show that available alternatives to the 2020 Census Disclosure Avoidance System would either fail to protect confidentiality, or would overly degrade the statistics' utility for the primary statutory use case: redrawing the boundaries of all of the nation's legislative and voting districts in compliance with the 1965 Voting Rights Act. You are reading the full technical report. For the summary paper see https://doi.org/10.1162/99608f92.4a1ebf70.
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An In-Depth Examination of Requirements for Disclosure Risk Assessment
October 2023
Authors:
Ron Jarmin,
John M. Abowd,
Ian M. Schmutte,
Jerome P. Reiter,
Nathan Goldschlag,
Victoria A. Velkoff,
Michael B. Hawes,
Robert Ashmead,
Ryan Cumings-Menon,
Sallie Ann Keller,
Daniel Kifer,
Philip Leclerc,
Rolando A. RodrÃguez,
Pavel Zhuravlev
Working Paper Number:
CES-23-49
The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. Following long-established precedent in economics and statistics, we argue that any proposal for quantifying disclosure risk should be based on pre-specified, objective criteria. Such criteria should be used to compare methodologies to identify those with the most desirable properties. We illustrate this approach, using simple desiderata, to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. Thus, more research is needed, but in the near-term, the counterfactual approach appears best-suited for privacy-utility analysis.
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A Simulated Reconstruction and Reidentification Attack on the 2010 U.S. Census
August 2025
Authors:
Lars Vilhuber,
John M. Abowd,
Ethan Lewis,
Nathan Goldschlag,
Michael B. Hawes,
Robert Ashmead,
Daniel Kifer,
Philip Leclerc,
Rolando A. RodrÃguez,
Tamara Adams,
David Darais,
Sourya Dey,
Simson L. Garfinkel,
Scott Moore,
Ramy N. Tadros
Working Paper Number:
CES-25-57
For the last half-century, it has been a common and accepted practice for statistical agencies, including the United States Census Bureau, to adopt different strategies to protect the confidentiality of aggregate tabular data products from those used to protect the individual records contained in publicly released microdata products. This strategy was premised on the assumption that the aggregation used to generate tabular data products made the resulting statistics inherently less disclosive than the microdata from which they were tabulated. Consistent with this common assumption, the 2010 Census of Population and Housing in the U.S. used different disclosure limitation rules for its tabular and microdata publications. This paper demonstrates that, in the context of disclosure limitation for the 2010 Census, the assumption that tabular data are inherently less disclosive than their underlying microdata is fundamentally flawed. The 2010 Census published more than 150 billion aggregate statistics in 180 table sets. Most of these tables were published at the most detailed geographic level'individual census blocks, which can have populations as small as one person. Using only 34 of the published table sets, we reconstructed microdata records including five variables (census block, sex, age, race, and ethnicity) from the confidential 2010 Census person records. Using only published data, an attacker using our methods can verify that all records in 70% of all census blocks (97 million people) are perfectly reconstructed. We further confirm, through reidentification studies, that an attacker can, within census blocks with perfect reconstruction accuracy, correctly infer the actual census response on race and ethnicity for 3.4 million vulnerable population uniques (persons with race and ethnicity different from the modal person on the census block) with 95% accuracy. Having shown the vulnerabilities inherent to the disclosure limitation methods used for the 2010 Census, we proceed to demonstrate that the more robust disclosure limitation framework used for the 2020 Census publications defends against attacks that are based on reconstruction. Finally, we show that available alternatives to the 2020 Census Disclosure Avoidance System would either fail to protect confidentiality, or would overly degrade the statistics' utility for the primary statutory use case: redrawing the boundaries of all of the nation's legislative and voting districts in compliance with the 1965 Voting Rights Act.
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SYNTHETIC DATA FOR SMALL AREA ESTIMATION IN THE AMERICAN COMMUNITY SURVEY
April 2013
Working Paper Number:
CES-13-19
Small area estimates provide a critical source of information used to study local populations. Statistical agencies regularly collect data from small areas but are prevented from releasing detailed geographical identifiers in public-use data sets due to disclosure concerns. Alternative data dissemination methods used in practice include releasing summary/aggregate tables, suppressing detailed geographic information in public-use data sets, and accessing restricted data via Research Data Centers. This research examines an alternative method for disseminating microdata that contains more geographical details than are currently being released in public-use data files. Specifically, the method replaces the observed survey values with imputed, or synthetic, values simulated from a hierarchical Bayesian model. Confidentiality protection is enhanced because no actual values are released. The method is demonstrated using restricted data from the 2005-2009 American Community Survey. The analytic validity of the synthetic data is assessed by comparing small area estimates obtained from the synthetic data with those obtained from the observed data.
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Access Methods for United States Microdata
August 2007
Working Paper Number:
CES-07-25
Beyond the traditional methods of tabulations and public-use microdata samples, statistical agencies have developed four key alternatives for providing non-government researchers with access to confidential microdata to improve statistical modeling. The first, licensing, allows qualified researchers access to confidential microdata at their own facilities, provided certain security requirements are met. The second, statistical data enclaves, offer qualified researchers restricted access to confidential economic and demographic data at specific agency-controlled locations. Third, statistical agencies can offer remote access, through a computer interface, to the confidential data under automated or manual controls. Fourth, synthetic data developed from the original data but retaining the correlations in the original data have the potential for allowing a wide range of analyses.
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Validating Abstract Representations of Spatial Population Data while considering Disclosure Avoidance
February 2020
Working Paper Number:
CES-20-05
This paper furthers a research agenda for modeling populations along spatial networks and expands upon an empirical analysis to a full U.S. county (Gaboardi, 2019, Ch. 1,2). Specific foci are the necessity of, and methods for, validating and benchmarking spatial data when conducting social science research with aggregated and ambiguous population representations. In order to promote the validation of publicly-available data, access to highly-restricted census microdata was requested, and granted, in order to determine the levels of accuracy and error associated with a network-based population modeling framework. Primary findings reinforce the utility of a novel network allocation method'populated polygons to networks (pp2n) in terms of accuracy, computational complexity, and real runtime (Gaboardi, 2019, Ch. 2). Also, a pseudo-benchmark dataset's performance against the true census microdata shows promise in modeling populations along networks.
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Improving Estimates of Neighborhood Change with Constant Tract Boundaries
May 2022
Working Paper Number:
CES-22-16
Social scientists routinely rely on methods of interpolation to adjust available data to their research needs. This study calls attention to the potential for substantial error in efforts to harmonize data to constant boundaries using standard approaches to areal and population interpolation. We compare estimates from a standard source (the Longitudinal Tract Data Base) to true values calculated by re-aggregating original 2000 census microdata to 2010 tract areas. We then demonstrate an alternative approach that allows the re-aggregated values to be publicly disclosed, using 'differential privacy' (DP) methods to inject random noise to protect confidentiality of the raw data. The DP estimates are considerably more accurate than the interpolated estimates. We also examine conditions under which interpolation is more susceptible to error. This study reveals cause for greater caution in the use of interpolated estimates from any source. Until and unless DP estimates can be publicly disclosed for a wide range of variables and years, research on neighborhood change should routinely examine data for signs of estimation error that may be substantial in a large share of tracts that experienced complex boundary changes.
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Exploring Administrative Records Use for Race and Hispanic Origin Item Non-Response
December 2014
Working Paper Number:
carra-2014-16
Race and Hispanic origin data are required to produce official statistics in the United States. Data collected through the American Community Survey and decennial census address missing data through traditional imputation methods, often relying on information from neighbors. These methods work well if neighbors share similar characteristics, however, the shape and patterns of neighborhoods in the United States are changing. Administrative records may provide more accurate data compared to traditional imputation methods for missing race and Hispanic origin responses. This paper first describes the characteristics of persons with missing demographic data, then assesses the coverage of administrative records data for respondents who do not answer race and Hispanic origin questions in Census data. The paper also discusses the distributional impact of using administrative records race and Hispanic origin data to complete missing responses in a decennial census or survey context.
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Why the Economics Profession Must Actively Participate in the Privacy Protection Debate
March 2019
Working Paper Number:
CES-19-09
When Google or the U.S. Census Bureau publish detailed statistics on browsing habits or neighborhood characteristics, some privacy is lost for everybody while supplying public information. To date, economists have not focused on the privacy loss inherent in data publication. In their stead, these issues have been advanced almost exclusively by computer scientists who are primarily interested in technical problems associated with protecting privacy. Economists should join the discussion, first, to determine where to balance privacy protection against data quality; a social choice problem. Furthermore, economists must ensure new privacy models preserve the validity of public data for economic research.
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The Nature of the Bias When Studying Only Linkable Person Records: Evidence from the American Community Survey
April 2014
Working Paper Number:
carra-2014-08
Record linkage across survey and administrative records sources can greatly enrich data and improve their quality. The linkage can reduce respondent burden and nonresponse follow-up costs. This is particularly important in an era of declining survey response rates and tight budgets. Record linkage also creates statistical bias, however. The U.S. Census Bureau links person records through its Person Identification Validation System (PVS), assigning each record a Protected Identification Key (PIK). It is not possible to reliably assign a PIK to every record, either due to insufficient identifying information or because the information does not uniquely match any of the administrative records used in the person validation process. Non-random ability to assign a PIK can potentially inject bias into statistics using linked data. This paper studies the nature of this bias using the 2009 and 2010 American Community Survey (ACS). The ACS is well-suited for this analysis, as it contains a rich set of person characteristics that can describe the bias. We estimate probit models for whether a record is assigned a PIK. The results suggest that young children, minorities, residents of group quarters, immigrants, recent movers, low-income individuals, and non-employed individuals are less likely to receive a PIK using 2009 ACS. Changes to the PVS process in 2010 significantly addressed the young children deficit, attenuated the other biases, and increased the validated records share from 88.1 to 92.6 percent (person-weighted).
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