The statbarn: A New Model for Output Statistical Disclosure Control

A major success for research this century has been the growth of secure facilities allowing research access to detailed sensitive personal data. This has also raised awareness of the problem of output disclosure risk, where statistics may inadvertently breach the confidentiality of data subjects, a risk that grows with the detail in the data. Managing this risk is a concern for these secure facilities. While there is a well-established literature on the protection of frequency tables and linear aggregates, researchers in secure facilities produce a wide range of statistical outputs. The theory covering non-tabular outputs is small, fractured, and has grown ad hoc. This is also reflected in the guidance available to data service staff, which typically consists of a long list of outputs and some rules to be applied to them. This paper describes a significant new concept in output statistical disclosure control: the statistical barn or ‘statbarn’. This is a framework to classify all statistical terms by their disclosure characteristics, including risk, exceptions and mitigation measures. This statbarn massively reduces the dimensionality of the disclosure checking problem, as well as providing improved clarity. It also creates a feasible basis for automatic disclosure control checking.