Quantifying Privacy in Nuclear Warhead Authentication Protocols
Abstract
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.
- Publication:
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arXiv e-prints
- Pub Date:
- November 2018
- DOI:
- 10.48550/arXiv.1811.10375
- arXiv:
- arXiv:1811.10375
- Bibcode:
- 2018arXiv181110375M
- Keywords:
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- Statistics - Other Statistics