  International verification of nuclear warheads is a practical problem in
which the protection of secret warhead information is of paramount importance.
We propose a measure that would enable a weapon owner to evaluate the privacy
of a proposed protocol in a technology-neutral fashion. We show the problem is
reducible to `natural' and `corrective' learning. The natural learning can be
computed without assumptions about the inspector, while the corrective learning
accounts for the inspector's prior knowledge. The natural learning provides the
warhead owner a useful lower bound on the information leaked by the proposed
protocol. Using numerical examples, we demonstrate that the proposed measure
correlates better with the accuracy of a maximum a posteriori probability
estimate than alternative measures.
