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Activity Number:
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14
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Health Policy Statistics
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| Abstract - #303796 |
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Title:
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Examining the Robustness of Fully Synthetic Data Techniques for Data with Binary Variables
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Author(s):
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Gregory Matthews*+ and Ofer Harel and Rob Aseltine
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Companies:
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University of Connecticut and University of Connecticut and University of Connecticut Health Center
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Address:
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, , ,
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Keywords:
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Synthetic Data ; Multiple Imputation
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Abstract:
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There is a growing demand for public use data while at the same time increasing concerns about the privacy of personal information. One proposed method for accomplishing both goals is to release data sets which do not contain real values but yield the same inferences as the actual data. The idea is to view confidential data as missing and use multiple imputation techniques to create synthetic data sets. In this paper, we compare techniques for creating synthetic data sets in simple scenarios with a binary variable.
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