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A neighborhood regression model for sample disclosure risk estimation

The disclosure risk involved in releasing data which consist of a sample from some population depends on both the sample and the population. When the sample is fully known, with only partial or no information on the population, a major problem in Statistical Disclosure Control (SDC) is the estimation of disclosure risk on the basis of the sample. Considering data in the form of a frequency table, risk arises from non-empty sample cells which represent small population cells (and population uniques in particular). Therefore risk estimation requires assessing which of the relevant population cells are indeed small. Various methods have been proposed for this task, and we present a new one, in which estimation of population cell frequencies is based on a model connecting the table parameters in neighborhoods defined in natural ways using the table structure and the nature of the variables. At this point this method is under experimentation, and we provide some preliminary comparisons with the Argus method in which inference is based on sampling weights, and with a log-linear models approach.