This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 85
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #308432
Title: Statistical Disclosure Risk of Synthetic Datasets
Author(s): Anne-Sophie Charest*+
Companies: Carnegie Mellon University
Address: 5414 Ellsworth Avenue Apt.2, Pittsburgh, PA, 15232, United States
Keywords: Multiple imputation ; Disclosure risk ; Synthetic datasets
Abstract:

When the idea of using multiple imputation for privacy purpose was first introduced, the claim was that if we release completely imputed (synthetic) datasets then confidentiality is protected because the individuals in the microdata do not correspond to any of the respondents. Privacy disclosure was taken for granted and most of the work focused on showing that synthetic datasets could provide accurate inferences. Several researchers have now come to the conclusion that there are risks of identity and attribute disclosure even when the released datasets are completely synthetic. In this paper, we look at different methods of assessing the disclosure risk from synthetic datasets, and study the efficiency of this technique to protect the privacy of respondents.


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