In statistical disclosure control of tabular data, sensitivity rules are commonly used to decide whether a table cell is sensitive and should therefore not be published. The most popular sensitivity rules are the dominance rule, the p%-rule and the pq-rule. The dominance rule has received critiques based on specific numerical examples and is being gradually abandoned by leading statistical agencies. In this paper, we construct general counterexamples which show that none of the above rules does adequately reflect disclosure risk if cell contributors or coalitions of them behave as intruders: in that case, releasing a cell declared non-sensitive can imply higher disclosure risk than releasing a cell declared sensitive. As possible solutions, we propose an alternative sensitivity rule based on the concentration of relative contributions. More generally, we suggest to complement a priori risk assessment based on sensitivity rules with a posteriori risk assessment which takes into account tables after they have been protected.
The probability of identification: applying ideas from forensic statistics to disclosure risk assessment
