Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census

Lin, Yue; Xiao, Ningchuan

doi:10.1007/s11113-023-09829-4

Cited by 4 publications

(7 citation statements)

References 31 publications

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“…This can facilitate precise estimation and analysis of population patterns, trends, and changes at the local level [Lomax and Smith, 2017;Wu et al, 2022]. Such data can also be used to empower policymakers and planners to simulate and evaluate the impact of various policies, interventions, or scenarios on individual behavior and movement within urban or regional contexts [He et al, 2020;Lin and Xiao, 2023a;Papyshev and Yarime, 2021;Tanton et al, 2009], thus supporting applications in domains such as public health [Grefenstette et al, 2013;Spooner et al, 2021] and transportation planning [Hörl andBalac, 2021, Zhu andFerreira, 2014]. In addition, there is existing literature on enhancing synthetic population data by incorporating census variables with external data sources such as health and commercial surveys [Spooner et al, 2021;Morrissey et al, 2015].…”

Section: Discussionmentioning

confidence: 99%

“…An optimization approach [Lin and Xiao, 2022; Lin and Xiao, 2023b; Lin, 2023] is used to construct the synthetic population data. We begin with a matrix representation of the individual-level population data that need to be synthesized.…”

Section: Optimization Modelingmentioning

confidence: 99%

“…The purpose of this paper is to describe a synthetic individual-level population dataset in the United States that is open and realistic and can be used to support small area estimation. This dataset is generated using a new method [Lin and Xiao, 2022; Lin and Xiao, 2023b] that solely relies on public aggregated data, which eliminates the need for individual-level survey data and can enhance replicability and generalizability across space and time. Specifically, we generate the synthetic data based on public census tables from the United States Census SF1.…”

Section: Introductionmentioning

confidence: 99%

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Synthetic population data for small area estimation in the United States

Lin

2023

Environment and Planning B: Urban Analytics and City Science

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Small area estimation is critical for a wide range of applications, including urban planning, funding distribution, and policy formulation. Individual-level population data, which typically include each individual’s socio-demographic characteristics and small area location, are a rich source of information for small area estimation. However, individual-level population data are often not made public due to confidentiality concerns. This paper describes the development of a public-use synthetic individual-level population dataset in the United States that can be useful for small area estimation. This dataset contains characteristics of housing type, age, sex, race, and Hispanic or Latino origin for all 308,745,538 individuals in the United States at the census block group level, based on publicly available aggregated data from the 2010 Census. Experimental results suggest the validity of the synthetic data by comparing it to different data sources, and we show examples of how this dataset can be used in small area estimation.

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Section: Discussionmentioning

confidence: 99%

Section: Optimization Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Synthetic population data for small area estimation in the United States

Lin

2023

Environment and Planning B: Urban Analytics and City Science

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“…Differential privacy is quite effective against the risk of reconstruction-abetted disclosure but the methodology is not fool proof. For example, Lin and Xiao (2023) have shown that the method, in this case the TopDown Algorithm (TDA) used by the U.S. Census Bureau, does not necessarily prevent the identification of population uniques using the public census tables, such as an individual belonging to a cell in a table that has a value of one. In particular, the probability of disclosure depends upon the size of the population, its composition and the number of attributes taken into account.…”

Section: Many Problems and One Solutionmentioning

confidence: 99%

Big Data, Demography, and Causality

Wunsch,

Gourbin,

Russo

2024

JSS

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The objectives of this paper are to examine to what extent Big Data are presently used in population research and to consider their potential for causal inference. After examining the characteristics and challenges of big data, the subsequent section deals with the use of big data in the study of the key demographic phenomena and is based on a literature review for the period 2015-2022 of 63 scientific journals concerned with population issues. The final section examines to what extent the use of big data could improve causal inference. Our results show that demographers continue to privilege sources of numerical data and are less prone to use digital media data or other sources such as images. Big Data can contribute to improving explanations in demography thanks to the large number of observations and variables in the data sets, especially when they can be individually linked together. Causal knowledge requires however that one can propose and test a suitable mechanism explaining why a variation in one variable produces a variation in another variable.

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“…In line with the Census Bureau's commitment to promoting public participation and ensuring “everyone counts,” measures have been taken to protect individual privacy by removing personal identifiers such as names and addresses from data releases. However, even without these identifiers, privacy concerns can still arise because the data contains information such as locations and demographic attributes that can be used to disclose the identities of individuals (Sweeney 2000; Lin and Harvey 2015; Lin and Xiao 2023a). An illustrative case is found in the research conducted by Abowd and Hawes (2023) on the 2010 U.S. Census data, which demonstrate that using a combination of census block, sex, and single year of age can differentiate a person from others in 44% of the national population.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Tradeoff Between Privacy and Utility of Complete‐count Census Data Using a Multiobjective Optimization Approach

Lin,

Xiao

2024

Geographical Analysis

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Privacy and utility are two important objectives to consider when releasing census data. However, these two objectives are often conflicting, as protecting privacy usually necessitates introducing noise into the data, which compromises data utility. Determining the appropriate level of privacy protection presents a significant challenge in the data release. Therefore, it is necessary to investigate the tradeoff between privacy and utility before making a final decision on the level of privacy protection. In this article, we propose a multiobjective optimization framework to generate multiple optimal solutions that satisfy the two objectives of privacy and utility, as well as to analyze the tradeoff between privacy and utility for decision‐making. This framework relocates individuals susceptible to revealing their identities to protect their privacy. We maximize the number of individuals relocated while maximizing the utility of the data after relocations. The proposed framework is tested using synthetic population data in Franklin County, Ohio. Our experimental results show that the framework can efficiently generate a collection of optimal solutions and can be used to effectively balance privacy and utility.

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Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census

Cited by 4 publications

References 31 publications

Synthetic population data for small area estimation in the United States

Synthetic population data for small area estimation in the United States

Big Data, Demography, and Causality

Exploring the Tradeoff Between Privacy and Utility of Complete‐count Census Data Using a Multiobjective Optimization Approach

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