Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads
Abstract Disarmament treaties have been the driving force towards reducing the large nuclear stockpile assembled during the Cold War. Further efforts are built around verification protocols capable of authenticating nuclear warheads while preventing the disclosure of confidential information. This t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-34679-7 |
| _version_ | 1797827502452768768 |
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| author | Gabriel V. Turturica Violeta Iancu |
| author_facet | Gabriel V. Turturica Violeta Iancu |
| author_sort | Gabriel V. Turturica |
| collection | DOAJ |
| description | Abstract Disarmament treaties have been the driving force towards reducing the large nuclear stockpile assembled during the Cold War. Further efforts are built around verification protocols capable of authenticating nuclear warheads while preventing the disclosure of confidential information. This type of problem falls under the scope of zero-knowledge protocols, which aim at multiple parties agreeing on a statement without conveying any information beyond the statement itself. A protocol capable of achieving all the authentication and security requirements is still not completely formulated. Here we propose a protocol that leverages the isotopic capabilities of NRF measurements and the classification abilities of neural networks. Two key elements guarantee the security of the protocol, the implementation of the template-based approach in the network’s architecture and the use of homomorphic inference. Our results demonstrate the potential of developing zero-knowledge protocols for the verification of nuclear warheads using Siamese networks on encrypted spectral data. |
| first_indexed | 2024-04-09T12:49:21Z |
| format | Article |
| id | doaj.art-542756ec0d864ee49f8e1cb80a4dfac2 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T12:49:21Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-542756ec0d864ee49f8e1cb80a4dfac22023-05-14T11:16:24ZengNature PortfolioScientific Reports2045-23222023-05-011311810.1038/s41598-023-34679-7Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheadsGabriel V. Turturica0Violeta Iancu1Extreme Light Infrastructure-Nuclear Physics, Horia Hulubei National Institute for R&D in Physics and Nuclear EngineeringExtreme Light Infrastructure-Nuclear Physics, Horia Hulubei National Institute for R&D in Physics and Nuclear EngineeringAbstract Disarmament treaties have been the driving force towards reducing the large nuclear stockpile assembled during the Cold War. Further efforts are built around verification protocols capable of authenticating nuclear warheads while preventing the disclosure of confidential information. This type of problem falls under the scope of zero-knowledge protocols, which aim at multiple parties agreeing on a statement without conveying any information beyond the statement itself. A protocol capable of achieving all the authentication and security requirements is still not completely formulated. Here we propose a protocol that leverages the isotopic capabilities of NRF measurements and the classification abilities of neural networks. Two key elements guarantee the security of the protocol, the implementation of the template-based approach in the network’s architecture and the use of homomorphic inference. Our results demonstrate the potential of developing zero-knowledge protocols for the verification of nuclear warheads using Siamese networks on encrypted spectral data.https://doi.org/10.1038/s41598-023-34679-7 |
| spellingShingle | Gabriel V. Turturica Violeta Iancu Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads Scientific Reports |
| title | Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads |
| title_full | Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads |
| title_fullStr | Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads |
| title_full_unstemmed | Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads |
| title_short | Homomorphic inference of deep neural networks for zero-knowledge verification of nuclear warheads |
| title_sort | homomorphic inference of deep neural networks for zero knowledge verification of nuclear warheads |
| url | https://doi.org/10.1038/s41598-023-34679-7 |
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