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Activity Number: 346
Type: Contributed
Date/Time: Tuesday, August 5, 2014 : 10:30 AM to 12:20 PM
Sponsor: Survey Research Methods Section
Abstract #313104 View Presentation
Title: Random Data Swapping as an Approach for Statistical Disclosure Limitation and Its Effects on Utility of Data
Author(s): Edward Grant and Joe Fred Gonzalez Jr.*+ and Guangyu Zhang and Alena Maze
Companies: University of Maryland and NCHS and NCHS/CDC and NCHS
Keywords: Data swapping ; Statistical Disclosure Limitation ; Utility of Data
Abstract:

Confidentiality is of primary importance for federal agencies, such as the National Center for Health Statistics (NCHS) when releasing microdata to the public. Although the data is initially de-identified, that is, stripped of personal identifying information (PII), such as name, address, social security number, etc., data users with malicious intent, also known as intruders, may be able to link records in the data to individuals through external data files or through observable traits. Records that have unique combinations of key variables are particularly vulnerable to disclosure. Failing to protect individuals from these breaches could possibly affect the reputation of the agency and may have consequences on the population's willingness to participate in future data collection efforts. Statistical disclosure limitation techniques, such as random data swapping, are used as an attempt to prevent breaches in confidentiality. For this presentation, random data swapping methods were explored through simulations and through real data analysis of public use data from the National Health Interview Survey (NHIS).


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