Disclosure Control Methods

Statistical agencies are interested in publishing useful statistical data but doing so may lead to the disclosure of individuals' private data. This is a problem as it leads to a trade-off between the utility of the published data and the risk of disclosure of confidential data. Disclosure control can be seen as the use of methods to deal with this problem by assessing and controlling the risk of disclosing confidential data while also providing researchers with useful statistical data. 

The goal of this workshop is to present an overview of disclosure control methods used when publishing tabular data. Methods of assessing cell risk will be presented. These include the standard cell sensitivity rules such as the nk and pq rules, generalized cell sensitivity rules using the precision-threshold-noise (PTN) framework and methods for determining sensitive aggregates of cells. Methods of controlling the risk of disclosure will also be presented. These include the use of cell suppression as well as the use of random tabular adjustment (RTA). A disclosure control framework using Bayesian decision theory will also be presented.

No laptops are required for this workshop.