Implementação fotónica de funções fisicamente não clonáveis

This dissertation aimed to study and develop optical Physically Unclonable Functions, which are physical devices characterized by having random intrinsic variations, thus being eligible towards high security systems due to their unclonability, uniqueness and randomness. With the rapid expansion of technologies such as Internet of Things and the concerns around counterfeited goods, secure and resilient cryptographic systems are in high demand. Moreover the development of digital ecosystems, mobile applications towards transactions now require fast and reliable algorithms to generate secure cryptographic keys. The statistical nature of speckle-based imaging creates an opportunity for these cryptographic key generators to arise. In the scope of this work, three different tokens were implemented as physically unclonable devices: tracing paper, plastic optical fiber and an organic-inorganic hybrid. These objects were subjected to a visible light laser stimulus and produced a speckle pattern which was then used to retrieve the cryptographic key associated to each of the materials. The methodology deployed in this work features the use of a Discrete Cosine Transform to enable a low-cost and semi-compact 128-bit key encryption channel. Furthermore, the authentication protocol required the analysis of multiple responses from different samples, establishing an optimal decision threshold level that maximized the robustness and minimized the fallibility of the system. The attained 128-bit encryption system performed, across all the samples, bellow the error probability detection limit of 10-12, showing its potential as a cryptographic key generator.Nesta dissertação pretende-se estudar e desenvolver Funções Fisicamente Não Clonáveis, dispositivos caracterizados por terem variações aleatórias intrínsecas, sendo, portanto, elegíveis para sistemas de alta segurança devido à sua impossibilidade de clonagem, unicidade e aleatoriedade. Com a rápida expansão de tecnologias como a Internet das Coisas e as preocupações com produtos falsificados, os sistemas criptográficos seguros e resilientes são altamente requisitados. Além disso, o desenvolvimento de ecossistemas digitais e de aplicações móveis para transações comerciais requerem algoritmos rápidos e seguros de geração de chaves criptográficas. A natureza estatística das imagens baseadas no speckle cria uma oportunidade para o aparecimento desses geradores de chaves criptográficas. No contexto deste trabalho, três dispositivos diferentes foram implementados como funções fisicamente não clonáveis, nomeadamente, papel vegetal, fibra ótica de plástico e um híbrido orgânico-inorgânico. Estes objetos foram submetidos a um estímulo de luz coerente na região espectral visível e produziram um padrão de speckle o qual foi utilizado para recuperar a chave criptográfica associada a cada um dos materiais. A metodologia implementada neste trabalho incorpora a Transformada Discreta de Cosseno, o que possibilita a criação de um sistema criptográfico de 128 bits caracterizado por ser semi-compacto e de baixo custo. O protocolo de autenticação exigiu a análise de múltiplas respostas de diferentes Physically Unclonable Functions (PUFs), o que permitiu estabelecer um nível de limite de decisão ótimo de forma a maximizar a robustez e minimizar a probabilidade de erro por parte do sistema. O sistema de encriptação de 128 bits atingiu valores de probabilidade de erro abaixo do limite de deteção, 10-12, para todas as amostras, mostrando o seu potencial como gerador de chaves criptográficas.Mestrado em Engenharia Físic

Random bit sequence generation from speckle patterns produced with multimode waveguides

With the rapid development of digital ecosystems, such as mobile applications towards goods/monetary transactions, a new paradigm of data transfer arises, which requires fast and reliable algorithms to generate random numbers. The statistical nature of speckle‐ based imaging creates an opportunity for these generators to arise as random number generators given the unpredictability and irreproducibility of such patterns. Hence, it is shown that the establishment of an experimental system is able to produce unique speckle patterns for remote cryptographic key storage and distribution, with a potential key rate generation of Gbs.publishe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost