Measuring disclosure risk with entropy in population based frequency tables

Abstract

Statistical agencies assess the risk of disclosure before releasing data. Unacceptably high disclosure risk will prevent a statistical agency from disseminating the data. The application of statistical disclosure control (SDC) methods aims to provide sufficient protection and make the data release possible. The disclosure risk of tabular data is typically quantified at the level of table cells. However, the evaluation of disclosure risk can require the assessment of the table as a whole, for example in the case of online flexible table generators. In this paper we use information theory to develop a disclosure risk measure for population-based frequency tables. The proposed disclosure risk measure quantifies the risk of attribute disclosure before and after an SDC method is applied. The new measure is compared to alternative disclosure risk measures developed at the Office for National Statistics.