Consistent Cell Means for Topcoded Incomes in the Public Use March CPS (1976-2007)

Using the internal March CPS, we create and in this paper distribute to the larger research community a cell mean series that provides the mean of all income values above the topcode for any income source of any individual in the public use March CPS that has been topcoded since 1976. We also describeour construction of this series. When we use this series together with the public use March CPS, we closely match the yearly mean income levels and income inequalities of the U.S. population found using the internal March CPS data.

The microdata analysis system at the U.S. Census Bureau

The U.S. Census Bureau has the responsibility to release high quality data products while maintaining the confidentiality promised to all respondents under Title 13 of the U.S. Code. This paper describes a Microdata Analysis System (MAS) that is currently under development, which will allow users to receive certain statistical analyses of Census Bureau data, such as crosstabulations and regressions, without ever having access to the data themselves. Such analyses must satisfy several statistical confidentiality rules; those that fail these rules will not be output to the user. In addition, the Drop q Rule, which requires removing a relatively small number of units before performing an analysis, is applied to all datasets. We describe the confidentiality rules and briefly outline an evaluation of the effectiveness of the Drop q Rule. We conclude with a description of other approaches to creating a system of this sort, and some directions for future research

Using linear programming methodology for disclosure avoidance purposes I

This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community. The views reflected in these reports are not necessarily those of the Census Bureau nor do they necessarily represent Census Bureau statistical policy or practice. Inquiries may be addressed to the author(s) or the SR

1. Introduction to Confidentiality, Census Bureau Data Products, and a Broad Definition of

The U.S. Census Bureau collects its survey and census data under Title 13 of the U.S. Code which promises confidentiality to its respondents. The agency also has the responsibility of releasing data for the purpose of statistical analysis. The goal is to release as much high quality data as possible without violating the pledge of confidentiality. We apply disclosure avoidance techniques prior to publicly releasing our data products to protect the confidentiality of our respondents and their data. This paper discusses the various types of data we releases, the disclosure avoidance techniques currently being used, and how they may be seen as a form of imputation

New Implementations of Noise for Tabular Magnitude Data, Synthetic Tabular Frequency and Microdata, and a Remote Microdata Analysis System

Disclaimer: This paper is released to inform interested parties of research and to encourage discussion. The view

Estimation of the Percent of Unique Population Elements on a Microdata File Using the Sample

This series contains research reports, written by or in cooperation with staff members of the Statistical Research Division, whose content may be of interest to the general statistical research community. The views reflected in these reports are not necessarily those of the Census Bureau nor do they necessarily represent Census Bureau statistical policy or practice. Inquiries may be addressed to the author(s) or the SRD Report Serie

Using noise for Disclosure Limitation of Establishment Tabular Data

The Bureau of the Census is looking into new methods of disclosure limitation for use with establishment tabular data. Currently we use a strategy that suppresses a cell in a table if the publication of that cell could potentially lead to the disclosure of an individual respondent's data. As an alternative to cell suppression that would allow us to publish more data and to fulfill more requests for special tabulations, we are experimenting with adding noise to our underlying microdata. By perturbing each respondent's data, we can provide protection to individual respondents without having to suppress cell totals. While adding noise is a much less complicated and time-consuming procedure than cell suppression, the question remains as to the utility of the data after noise is added. To preserve the quality of aggregate estimates that would not normally be at risk for disclosure, we tested the option of forcing estimates at certain levels of aggregation to equal their true values before the addition of noise. Interior table cells were then raked to these aggregate cells. In this paper we discuss the advantages and disadvantages of adding noise to microdata as compared to cell suppression, and we describe the results of using noise and raking with the Research and Development survey. KEYWORDS