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.
Cite
@article{arxiv.1811.10375,
title = {Quantifying Privacy in Nuclear Warhead Authentication Protocols},
author = {Ruaridh. R Macdonald and R. Scott Kemp},
journal= {arXiv preprint arXiv:1811.10375},
year = {2018}
}