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Resource Library
Center for Disclosure Avoidance Research (CDAR) Working Papers
Center for Disclosure Avoidance Research (CDAR) Working Papers
Disclosure avoidance techniques are used to ensure published data cannot be used to estimate an individual firm's data too closely.
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.
Working Paper
Disclosure Avoidance Techniques at the U.S. Census Bureau
The U.S. Census Bureau collects its survey and census data under the U.S. Code’s Title 13, which promises confidentiality to its respondents.
Working Paper
On Invariant Post Randomization for Statistical Disclosure Control
In this paper, we investigate certain operational and inferential aspects of invariant PRAM as a tool for disclosure limitation of categorical data.
Working Paper
Likelihood-Based Finite Sample Inference
Likelihood-based finite sample inference based on synthetic data under the exponential model is developed in this paper.
Working Paper
Noise Multiplication for Statistical Disclosure Control
In this article multiplication of original data values by random noise is suggested as a disclosure control strategy when only the top of the data is sensitive.
Working Paper
Likelihood Based Inference Under Noise Multiplication
When statistical agencies release microdata to the public, a major concern is the control of disclosure risk, while ensuring utility in the released data.
Working Paper
Statistical Analysis of Noise Multiplied Data
A statistical analysis of data that have been multiplied by randomly drawn noise variables in order to protect the confidentiality of individual values.
Working Paper
Suppression vs. Random Rounding Disclosure-Avoidance Alternatives
Working Paper
Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing
Page Last Revised - October 28, 2021
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