Examining the robustness of fully synthetic data techniques for data with binary variables

Abstract

There is a growing demand for public use data while at the same time there are increasing concerns about the privacy of personal information. One proposed method for accomplishing both goals is to release data sets that 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 article, we compare techniques for creating synthetic data sets in simple scenarios with a binary variable.

Keywords:

• synthetic data
• disclosure limitation
• confidentiality
• multiple imputation

AMS Subject Classification: :

• 65C60