Towards Providing Automated Feedback on the Quality of Inferences from Synthetic Datasets

Many national statistical agencies release data to the public that have been altered to protect the confidentiality of data subjects’ identities and sensitive attributes. Unfortunately, for methods of disclosure limitation in practice, it is typically impossible for analysts to gauge how the disclosure limitation has compromised the quality of inferences from the altered data alone. This is particularly problematic when data are intensely redacted to protect confidentiality. Without a sense of data quality, analysts cannot determine if they should trust the analysis; even worse, analysts who blindly trust the results could be led to incorrect conclusions.