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3 results for “Physical Unclonable Functions”
Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification - Dataset
<p>This is the dataset of "Artificial fingerprints engraved through block-copolymers as nanoscale physical unclonable functions for authentication and identification" by Irdi Murataj, Chiara Magosso, Stefano Carignano, Matteo Fretto, Federico Ferrarese Lupi, and Gianluca Milano, Nature Communications (2024), DOI: 10.1038/s41467-024-54492-8</p> <p>Part of this was funded by the project MEMQuD, code 20FUN06. The project has received funding from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program.</p> <p>Part of this work was supported by the European project OpMetBat, code 21GRD01. The project has received funding from the European Partnership on Metrology, cofinanced from the the European Union's Horizon Europe Research and Innovation Programme, and by Participating States.</p> <p>Part of this work was supported by the European Union - Next Generation EU under the National Recovery and Resilience Plan (NRRP), Mission 04 Component 2 Investment 3.1 | Project Code: IR0000027 - CUP: B33C22000710006 - iENTRANCE@ENL: Infrastructure for Energy TRAnsition aNd Circular Economy @EuroNanoLab.</p>
Quantifying the Sensitivity and Unclonability of Optical Physical Unclonable Functions (DATA and Python codes)
<p>The data contain experimental and numerical challenge response pairs (CRPs) collected by experimental setup and using Puffraction homebuilt code. In the data set is available all CRPs referred to the paper entitled "Quantifying the Sensitivity and Unclonability of Optical Physical Unclonable Functions" by Giuseppe Emanuele Lio, Sara Nocentini, Lorenzo Pattelli, Eleonora Cara, Diederik Sybolt Wiersma, Ulrich R"uhrmair, and Francesco Riboli. </p> <p> </p> <p>The <strong><em>puffractio</em></strong> python code used to generate and process the numerical data is available at the following link <a href="https://github.com/lpattelli/puffractio.git">https://github.com/lpattelli/puffractio.git</a></p> <p> </p> <p>Please cite the following paper: </p> <p>Quantifying the Sensitivity and Unclonability of Optical Physical Unclonable Functions</p> <p><a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Lio%2C+Giuseppe+Emanuele">Giuseppe Emanuele Lio</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Nocentini%2C+Sara">Sara Nocentini</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Pattelli%2C+Lorenzo">Lorenzo Pattelli</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Cara%2C+Eleonora">Eleonora Cara</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Wiersma%2C+Diederik+Sybolt">Diederik Sybolt Wiersma</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=R%C3%BChrmair%2C+Ulrich">Ulrich Rührmair</a>, <a href="https://onlinelibrary.wiley.com/action/doSearch?ContribAuthorRaw=Riboli%2C+Francesco">Francesco Riboli</a></p> <p><a href="https://onlinelibrary.wiley.com/doi/full/10.1002/adpr.202200225">https://onlinelibrary.wiley.com/doi/full/10.1002/adpr.202200225</a></p>
Octopus: A Novel Approach for Health Data Masking and Retrieving using Physically Unclonable Function and Machine Learning
<p>The health equipment is used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) techniques are used to retrieve the health data back and reduce security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.</p>
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