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Light in, sound keys out: photoacoustic PUFs from stochastic nanocomposites
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Park, Taehyun | - |
| dc.contributor.author | Kim, Junhyung | - |
| dc.contributor.author | Ko, Raksan | - |
| dc.contributor.author | Park, Byullee | - |
| dc.contributor.author | Yoo, Hocheon | - |
| dc.date.accessioned | 2025-09-09T03:00:12Z | - |
| dc.date.available | 2025-09-09T03:00:12Z | - |
| dc.date.issued | 2025-08 | - |
| dc.identifier.issn | 2041-1723 | - |
| dc.identifier.issn | 2041-1723 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/208682 | - |
| dc.description.abstract | We present a concept of physically unclonable functions utilizing the photoacoustic effect to generate structurally random, inference-resistant cryptographic keys. The system consists of a CuO/SnO2 nanoparticle composite, where CuO acts as a visible-range absorber and SnO2 serves as a non-absorbing dispersive matrix. Nanosecond laser pulses induce localized heating and acoustic wave emission, providing spatially heterogeneous photoacoustic signals that are digitized into binary matrices. Evaluations across ten devices yielded a bit uniformity of 49.54%, inter-device Hamming distance of 49.69%, entropy of 0.983, and bit aliasing of 49.38%-all approaching ideal values for secure key generation. Machine learning attacks using logistic regression and support vector machines failed to infer underlying patterns, with prediction accuracies of 53.53% and 52.54%. The device maintains cryptographic performance after transfer to diverse substrates, including human skin, highlighting its mechanical adaptability. This subsurface, light-to-sound-based approach offers a scalable platform for secure authentication on flexible or opaque surfaces. | - |
| dc.format.extent | 11 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Nature Publishing Group | - |
| dc.title | Light in, sound keys out: photoacoustic PUFs from stochastic nanocomposites | - |
| dc.type | Article | - |
| dc.publisher.location | 영국 | - |
| dc.identifier.doi | 10.1038/s41467-025-62747-1 | - |
| dc.identifier.scopusid | 2-s2.0-105012854184 | - |
| dc.identifier.wosid | 001549206500012 | - |
| dc.identifier.bibliographicCitation | Nature Communications, v.16, no.1, pp 1 - 11 | - |
| dc.citation.title | Nature Communications | - |
| dc.citation.volume | 16 | - |
| dc.citation.number | 1 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.endPage | 11 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Science & Technology - Other Topics | - |
| dc.relation.journalWebOfScienceCategory | Multidisciplinary Sciences | - |
| dc.subject.keywordPlus | copper oxide | - |
| dc.subject.keywordPlus | nanocomposite | - |
| dc.subject.keywordPlus | nanoparticle | - |
| dc.subject.keywordPlus | tin oxide | - |
| dc.subject.keywordAuthor | Copper Oxide | - |
| dc.subject.keywordAuthor | Tin Oxide | - |
| dc.subject.keywordAuthor | Copper Oxide | - |
| dc.subject.keywordAuthor | Nanocomposite | - |
| dc.subject.keywordAuthor | Nanoparticle | - |
| dc.subject.keywordAuthor | Tin Oxide | - |
| dc.subject.keywordAuthor | Acoustic Wave | - |
| dc.subject.keywordAuthor | Heating | - |
| dc.subject.keywordAuthor | Instrumentation | - |
| dc.subject.keywordAuthor | Regression Analysis | - |
| dc.subject.keywordAuthor | Article | - |
| dc.subject.keywordAuthor | Controlled Study | - |
| dc.subject.keywordAuthor | Cryptography | - |
| dc.subject.keywordAuthor | Machine Learning | - |
| dc.subject.keywordAuthor | Photoacoustics | - |
| dc.subject.keywordAuthor | Support Vector Machine | - |
| dc.subject.keywordAuthor | Article | - |
| dc.subject.keywordAuthor | Entropy | - |
| dc.subject.keywordAuthor | Human | - |
| dc.subject.keywordAuthor | Laser | - |
| dc.subject.keywordAuthor | Light | - |
| dc.subject.keywordAuthor | Logistic Regression Analysis | - |
| dc.subject.keywordAuthor | Prediction | - |
| dc.subject.keywordAuthor | Sound | - |
| dc.identifier.url | https://www.nature.com/articles/s41467-025-62747-1 | - |
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