Evolution on the Generation and Analysis of Single Imputation Synthetic Datasets in Statistical Disclosure Control

We present an overview of the evolution of single imputation synthetic datasets in Statistical Disclosure Control (SDC). Imputation is a widely used technique for generating privacy-preserving synthetic data that allows for the release of statistical information while protecting individual privacy, and we will focus on the evolution of the techniques on generating only one single dataset available to release and the evolution of the methods that allow its analysis. While the review does not delve into specific practical aspects or implementations, it provides a comprehensive understanding of the evolution in the generation and analysis of single imputation synthetic datasets. By synthesizing the existing literature, the paper aims to contribute to the knowledge base in SDC and assist researchers and practitioners in making informed decisions regarding the generation and analysis of synthetic datasets for statistical purposes.***