  When a sample frequency table is published, disclosure risk arises when some
individuals can be identified on the basis of their values in certain
attributes in the table called key variables, and then their values in other
attributes may be inferred, and their privacy is violated. On the basis of the
sample to be released, and possibly some partial knowledge of the whole
population, an agency which considers releasing the sample, has to estimate the
disclosure risk. Risk arises from non-empty sample cells which represent small
population cells and from population uniques in particular. Therefore risk
estimation requires assessing how many of the relevant population cells are
likely to be small. Various methods have been proposed for this task, and we
present a method in which estimation of a population cell frequency is based on
smoothing using a local neighborhood of this cell, that is, cells having
similar or close values in all attributes. We provide some preliminary results
and experiments with this method. Comparisons are made to two other methods: 1.
a log-linear models approach in which inference on a given cell is based on a
``neighborhood'' of cells determined by the log-linear model. Such
neighborhoods have one or some common attributes with the cell in question, but
some other attributes may differ significantly. 2 The Argus method in which
inference on a given cell is based only on the sample frequency in the specific
cell, on the sample design and on some known marginal distributions of the
population, without learning from any type of ``neighborhood'' of the given
cell, nor from any model which uses the structure of the table.
