The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census

Kenny, Christopher T.; Kuriwaki, Shiro; McCartan, Cory; Rosenman, Evan T. R.; Simko, Tyler; Imai, Kosuke

doi:10.1126/sciadv.abk3283

Cited by 79 publications

(88 citation statements)

References 33 publications

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“…Note that this result is supported by Kenny et al [5], which also predicts individual race and ethnicity, but additionally uses analysis of names to help predict race and ethnicity. Kenny et al found that TDA (using versions with more noise than the final version we tested) did not degrade the quality of these predictions.…”

Section: Our Simple Inference Non-attacksupporting

confidence: 66%

A Note on the Misinterpretation of the US Census Re-identification Attack

Francis¹

2022

Preprint

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In 2018, the US Census Bureau designed a new data reconstruction and re-identification attack and tested it against their 2010 data release. The specific attack executed by the Bureau allows an attacker to infer the race and ethnicity of respondents with average 75% precision for 85% of the respondents, assuming that the attacker knows the correct age, sex, and address of the respondents. They interpreted the attack as exceeding the Bureau's privacy standards, and so introduced stronger privacy protections for the 2020 Census in the form of the TopDown Algorithm (TDA).This paper demonstrates that race and ethnicity can be inferred from the TDA-protected census data with substantially better precision and recall, using less prior knowledge: only the respondents' address. Race and ethnicity can be inferred with average 75% precision for 98% of the respondents, and can be inferred with 100% precision for 11% of the respondents. The inference is done by simply assuming that the race/ethnicity of the respondent is that of the majority race/ethnicity for the respondent's census block.The conclusion to draw from this simple demonstration is NOT that the Bureau's data releases lack adequate privacy protections. Indeed it is the purpose of the data releases to allow this kind of inference. The problem, rather, is that the Bureau's criteria for measuring privacy is flawed and overly pessimistic.

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Section: Our Simple Inference Non-attacksupporting

confidence: 66%

A Note on the Misinterpretation of the US Census Re-identification Attack

Francis¹

2022

Preprint

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“…For example, Kenny et al (2021) used the April 2021 version of the 2010 Census demonstration data to examine both the properties of these data for purposes of redistricting and to simulate alternative redistricting maps that conform to commonly used criteria for legally acceptable redistricting practices. They find systematic biases in these data along racial and partisan lines that reduce the heterogeneity by geography in both of these dimensions.…”

Section: Assessments Of 2010 Demonstration Data Products For Redistri...mentioning

confidence: 99%

“…Furthermore, Kenny et al (2021) examined the final version of the 2010 demonstration data released on August 12, 2021, that was produced using the version of the Census's TDA that formed the basis of the DAS for the 2020 Census redistricting data, including its greater privacy loss budget. These authors found that while this final demonstration product was an improvement over previous releases, it still produced biases in drawing and simulation of voting districts (VTDs).…”

Section: Assessments Of 2010 Demonstration Data Products For Redistri...mentioning

confidence: 99%

A Chronicle of the Application of Differential Privacy to the 2020 Census

Hotz

Salvo

2022

Harvard Data Science Review

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In this article, we chronicle the U.S. Census Bureau's development of the Disclosure Avoidance System (DAS) for the publicly released products of the 2020 Census of Population. We provide a brief history of the Census Bureau's fulfillment of its dual mission of conducting and disseminating the constitutionally mandated decennial information on the U.S. population and its promise of safeguarding the confidentiality of that information. We discuss the basis for and development of a new DAS for released data products from the 2020 Census and the evidence that emerged from various user communities on the accuracy and usability of data produced under this new DAS. We offer some assessments of this experience, the dilemmas and challenges that the Census Bureau faces for producing usable data while safeguarding the confidentiality of the information it collects, and some recommendations for addressing these challenges in the future.

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“…Quantitative geographers may be among the most impacted researchers, given their focus on aggregate geographic data, but the Census Bureau Disclosure Avoidance System and differential privacy are likely to have wide-reaching effects for non-quantitative researchers and policymakers, as well. Preliminary research has suggested that uncertainty may be much higher for certain places and groups, for example, indicating decadal population loss when none has occurred, for small towns and indigenous areas (Wezerek and Van Riper, 2020) or mis-characterizing population counts and characteristics in political redistricting (Kenny et al, 2021). In estimating possible effects on county-to-county migration data, Winkler et al (2021) find that uncertainty is potentially higher for Hispanic migrants, as well as the young and old.…”

Section: Uncertainty Is Dead; Long Live Uncertaintymentioning

confidence: 99%

“…3.As Kenny et al (2021) point out, the US Census Bureau has long adjusted data (e.g. record swapping) to help ensure confidentiality, however, the new Disclosure Avoidance System, which combines injecting noise into data with post-processing adjustments, is widely agreed to be an entirely different sort of confidentiality protection beast.…”

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confidence: 99%

Quantitative methods I: Reckoning with uncertainty

Franklin

2022

Progress in Human Geography

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In this report, I address the timeless topic of uncertainty. Quantitative methods are in flux and uncertainty provides a useful vantage point for assessing these changes. I draw particular attention to two forces at odds with each other: on the one hand, innovative methods and data that allow researchers to embrace uncertainty, and on the other, recent trends towards deliberate amplification of uncertainty, in the form of differential privacy methods which potentially limit our capacity for measuring and understanding the world around us. I close by highlighting some implications for users and producers of quantitative methods and data in geography.

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The use of differential privacy for census data and its impact on redistricting: The case of the 2020 U.S. Census

Abstract: New Census privacy protections may introduce both bias and noise into redistricting and voting rights analysis.

Cited by 79 publications

References 33 publications

A Note on the Misinterpretation of the US Census Re-identification Attack

A Note on the Misinterpretation of the US Census Re-identification Attack

A Chronicle of the Application of Differential Privacy to the 2020 Census

Quantitative methods I: Reckoning with uncertainty

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