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.
Modernization of Statistical Disclosure Limitation at US Census Bureau
The Census Bureau is developing formally private SDL methods to protect its data products.
Disclosure Review for Qualitative Research
Baseline document on disclosure avoidance procedures for qualitative research in particular, and suggestions for implementation.
After DA techniques are employed, it can be useful to conduct a motivated intruder reidentification study to assess the disclosure risk of data products.
Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing
Predicting Complementary Cell Suppressions
The U.S. Census Bureau uses cell suppression methodology as the primary disclosure avoidance methodology for various economic surveys.
Evaluating a Remote Access System
The U.S. Census Bureau has the dual aims of releasing useful data while protecting respondent confidentiality.
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.
Emerging Applications of Randomized Response Concepts
Randomized response (RR) was introduced as a technique for protecting respondents' privacy in survey interviews regarding sensitive characteristics.
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.
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.
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.
Likelihood-Based Finite Sample Inference
Likelihood-based finite sample inference based on synthetic data under the exponential model is developed in this 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.
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.
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.
A Comparison of Statistical Disclosure Control Methods: Multiple Im...
A Comparison of Statistical Disclosure Control Methods: Multiple Imputation Versus Noise Multiplication