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Resource Library
Disclosure Avoidance Working Papers
Disclosure Avoidance Working Papers
U.S. Census Bureau "Working Papers" have not undergone the review and editorial process generally accorded official Census Bureau publications. These working papers are intended to make results of Census Bureau research available to others and to encourage discussion on disclosure avoidance.
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Working Paper
Disclosure Avoidance for the 2020 Census DHC
In this paper we describe the updates to the DAS that were required to release the Demographic and Housing Characteristics File.
Working Paper
Preliminary Report on Differentially Private Post-Stratification
Working Paper
Database Reconstruction Does Compromise Confidentiality
Working Paper
21st Century Statistical Disclosure Limitation
Working Paper
An Annotated Bibliography of Privacy-Violating Attacks
This annotated bibliography is intended to serve as a convenient guide to papers constituting the scientific literature on privacy-violating attacks.
Working Paper
Bayesian and Frequentist Semantics for Common Variations of DP
Differential privacy is the formal privacy framework adopted for disclosure avoidance in the 2020 United States Decennial Census of Population and Housing.
Working Paper
Confidentiality Protection in the 2020 U.S. Census of Population
This article discusses the deployment of a differential privacy framework for the 2020 US Census that was customized to protect confidentiality.
Working Paper
Information Design for Differential Privacy
We consider the problem of choosing such a mechanism so as to maximize the value of its output to end users.
Working Paper
The 2020 Census Disclosure Avoidance System TopDown Algorithm
The Census TopDown Algorithm is a disclosure avoidance system using differential privacy for privacy-loss accounting.
Working Paper
A Penny Synthesized is a Penny Earned?
The Census Bureau has expressed interest in using modern synthetic data modeling techniques for privacy and confidentiality protection in future releases.
Working Paper
An Uncertainty Principle is a Price of Privacy-Preserving Microdata
We show that an uncertainty principle governs the trade-off between accuracy for a population of interest vs. accuracy for its component sub-population.
Working Paper
Geographic Spines in the 2020 Census Disclosure Avoidance System
The Census TopDown Algorithm outputs microdata that satisfy either pure or zero-concentrated differential privacy.
Working Paper
Synthesizing Familial Linkages for Privacy in Microdata
Synthetic data has become a successful way to provide external researchers a chance to conduct a wide variety of analyses on microdata.
Working Paper
Probability Forecast Combination via Entropy Regularized Wasserstein
In this paper, we study a class of density forecast combination methods based on a Wasserstein metric.
Working Paper
The Modernization of Statistical Disclosure Limitation
The U.S. Census Bureau views disclosure limitation not just as a research interest, but
as an operational imperative.
Working Paper
Differentially Private k-Nearest Neighbor Missing Data Imputation
In this paper, we establish a smooth upper bound for k-nearest-neighbor imputation.
Working Paper
Reidentification Primer Using Four Metrics
This working paper updates our methodology for re-identification studies.
Working Paper
Disclosure Review for Qualitative Research
Baseline document on disclosure avoidance procedures for qualitative research in particular, and suggestions for implementation.
Working Paper
A History of the American Housing Survey and Disclosure Avoidance
The U.S. Census Bureau conducts the American Housing Survey (AHS) for the Department of Housing and Urban Development (HUD) under Title 13, U.S. Code, Section 9
Working Paper
A History of the Current Population Survey and Disclosure Avoidance
The U.S. Census Bureau conducts the monthly Current Population Survey (CPS) for the Bureau of Labor Statistics under Title 13, U.S. Code, Section 9
Working Paper
A History of the Economic Census and Disclosure Avoidance
The U.S. Census Bureau conducts the Economic Census under Title 13, U.S. Code, Section 9.
Working Paper
A History of the U.S. Census Bureau's Disclosure Review Board
The Board ensures that standard DA techniques have been applied, but its members also discuss each product to determine if it presents any additional disclosure
Working Paper
Disclosure Avoidance Techniques Used for the 1960 Through 2010 Census
The U.S. Census Bureau conducts the decennial censuses under Title 13, U.S. Code, Section 9.
Working Paper
Legacy Techniques and Current Research in Disclosure Avoidance
The Census Bureau applies Disclosure Avoidance (DA) techniques to its publicly released statistical products in order to protect respondent confidentiality..
Working Paper
Reidentification Studies
The U.S. Census Bureau conducts its censuses and surveys under Title 13, U.S. Code, Section 9.
Working Paper
Application Level Cryptography for Securing Online Survey Responses
Working Paper
The Creation and Use of the SIPP Synthetic Beta v7.0
Working Paper
Rosetta Wiki 1.0 User Guide
Working Paper
Formal Privacy and Synthetic Data for the American Community Survey
Working Paper
Synthesizing Housing Units for the American Community Survey
Working Paper
Philosophy of Disclosure Avoidance for Census Bureau Data
Working Paper
Predicting Complementary Cell Suppressions
The U.S. Census Bureau uses cell suppression methodology as the primary disclosure avoidance methodology for various economic surveys.
Working Paper
Evaluating a Remote Access System
The U.S. Census Bureau has the dual aims of releasing useful data while protecting respondent confidentiality.
Working Paper
A Concise Theory of Randomized Response Techniques for Privacy
A variety of randomized response (RR) procedures for privacy and confidentiality protection have been proposed, studied and compared in the literature.
Working Paper
Emerging Applications of Randomized Response Concepts
Randomized response (RR) was introduced as a technique for protecting respondents' privacy in survey interviews regarding sensitive characteristics.
Working Paper
Measuring Identification Risk in Microdata Release and Its Control
Statistical agencies often release a masked or perturbed version of survey data to protect respondents' confidentiality.
Page Last Revised - November 18, 2021
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