Measuring Disclosure Risk and Information Loss in Population Based Frequency Tables

Frequency tables disseminated by statistical agencies have always been of high interest. However, the agencies have to ensure that the risk of identifying individuals and disclosing individuals’ attributes from the released data is low. Therefore they assess the risk of disclosure and apply statistical disclosure control (SDC) methods if necessary. The main objective of this work is to measure dislosure risk in population based frequency tables. The disclosure risk assessment of such tables is often based on the so-called threshold rule. A cell of the table is of high disclosure risk according to this rule if the cell value does not exceed a certain threshold, for example 2. In this work we propose to measure the disclosure risk in an alternative way. Our approach takes the entire table (and rows/columns of the table) into consideration. We introduce a disclosure risk measure, which is based on information theoretical definitions, such as the entropy and the conditional entropy. There are two main types of SDC methods. Pre-tabular methods, such as record swapping, alter the values of a variable (or more variables) for selected individuals