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Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface
Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769542/ https://www.ncbi.nlm.nih.gov/pubmed/35044819 http://dx.doi.org/10.1126/sciadv.abl9874 |
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author | Dong, Bowei Zhang, Zixuan Shi, Qiongfeng Wei, Jingxuan Ma, Yiming Xiao, Zian Lee, Chengkuo |
author_facet | Dong, Bowei Zhang, Zixuan Shi, Qiongfeng Wei, Jingxuan Ma, Yiming Xiao, Zian Lee, Chengkuo |
author_sort | Dong, Bowei |
collection | PubMed |
description | Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication. |
format | Online Article Text |
id | pubmed-8769542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-87695422022-02-01 Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface Dong, Bowei Zhang, Zixuan Shi, Qiongfeng Wei, Jingxuan Ma, Yiming Xiao, Zian Lee, Chengkuo Sci Adv Physical and Materials Sciences Security is a prevailing concern in communication as conventional encryption methods are challenged by progressively powerful supercomputers. Here, we show that biometrics-protected optical communication can be constructed by synergizing triboelectric and nanophotonic technology. The synergy enables the loading of biometric information into the optical domain and the multiplexing of digital and biometric information at zero power consumption. The multiplexing process seals digital signals with a biometric envelope to avoid disrupting the original high-speed digital information and enhance the complexity of transmitted information. The system can perform demultiplexing, recover high-speed digital information, and implement deep learning to identify 15 users with around 95% accuracy, irrespective of biometric information data types (electrical, optical, or demultiplexed optical). Secure communication between users and the cloud is established after user identification for document exchange and smart home control. Through integrating triboelectric and photonics technology, our system provides a low-cost, easy-to-access, and ubiquitous solution for secure communication. American Association for the Advancement of Science 2022-01-19 /pmc/articles/PMC8769542/ /pubmed/35044819 http://dx.doi.org/10.1126/sciadv.abl9874 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Physical and Materials Sciences Dong, Bowei Zhang, Zixuan Shi, Qiongfeng Wei, Jingxuan Ma, Yiming Xiao, Zian Lee, Chengkuo Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
title | Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
title_full | Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
title_fullStr | Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
title_full_unstemmed | Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
title_short | Biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
title_sort | biometrics-protected optical communication enabled by deep learning–enhanced triboelectric/photonic synergistic interface |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8769542/ https://www.ncbi.nlm.nih.gov/pubmed/35044819 http://dx.doi.org/10.1126/sciadv.abl9874 |
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