JSM 2005 Online Program

Abstract #304511

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	367
Type:	Contributed
Date/Time:	Wednesday, August 10, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Survey Research Methods
Abstract - #304511
Title:	Disclosure Risks in Releasing Output Based on Regression Residuals
Author(s):	Arnold Reznek*+ and T. Lynn Riggs
Companies:	U.S. Census Bureau and U.S. Census Bureau
Address:	6205 Seminole Place, Berwyn Heights, MD, 20740, United States
Keywords:	confidentiality ; disclosure risk ; regression models
Abstract:	Previous research by the authors has demonstrated that disclosure risks can exist in regression models, including Generalized Linear Models (GLMs). Model coefficients of fully interacted dummy variables can be risky, as can correlation and covariance matrices and variance-covariance matrices of models coefficients. This paper extends these results by considering other types of model output, such as partial correlation coefficients and output involving model residuals. This area of research is becoming increasingly important as statistical agencies recognize the risks associated with releasing public-use files and move to other models of restricted access to confidential data (i.e., research data centers, model servers). While these methods limit the risk of disclosing confidential data, further research is needed to avoid unintended disclosure when releasing model diagnostics. This paper helps inform this line of research.

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