Beschreibung

"Many agencies are investigating whether releasing synthetic microdata could be a viable dissemination strategy for highly sensitive data, such as business data, for which disclosure avoidance regulations otherwise prohibit the release of public use microdata. However, existing methods assume that the original data either cover the entire population or comprise a simple random sample, which limits the application of these methods in the context of survey data with unequal weights. This paper discusses synthetic data generation under informative sampling. To utilise design information in survey weights, we rely on the pseudo likelihood approach when building a hierarchical Bayesian model to estimate the distribution of the finite population. Then, synthetic populations are randomly drawn from the estimated finite population density. We present the full conditional distributions of the Markov chain Monte Carlo algorithm for posterior inference with the pseudo likelihood function. Using simulation studies, we show that the suggested synthetic data approach offers high utility for design‐based and model‐based analyses while offering a high level of disclosure protection. We apply the proposed method to a subset of the 2012 U.S. Economic Census and evaluate results with utility metrics and disclosure avoidance metrics under data attacker scenarios commonly used for business data." (Author's abstract, IAB-Doku) ((en))