Balancing Disclosure Risk Against the Loss of Nonpublication

A nondisclosure policy for tabular data or microdata restricts release of information that could be related to a specific individual. Pannekoek and de Waal (1998) describe a rule that suppresses data release when the number of people in a cell defined by a rare characteristic falls below a fixed floor, and show how empirical Bayes methods can be used to improve the estimation of that number. We argue that the nondisclosure problem can be formulated as a decision problem in which one loss is associated with the possibility of disclosure and another with nonpublication of data. This analysis supports a decision on whether to disclose information in each cell, minimizing the expected sum of the two losses. We present arguments for several loss functions, considering both tabular and microdata releases, and illustrate their application to simple simulated data.