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Waste oils are considered a promising and sustainable source of raw materials in the biofuel industry. In recent years, research has been focused on the conversion of free fatty acids (FFAs) in fish oil to fatty acid methyl esters (FAMEs) mainly through the transesterification reaction. The biodiesel produced from salmon wastes provides an alternative source of raw material for sustainable energy production, meeting the growing energy demand without damaging the environment. The aim of this work is to investigate biodiesel production from salmon oil through a complete experimental design of the transesterification process. The biodiesel production was carried out by a homogeneous alkaline transesterification process using methanol and potassium methylate as a catalyst. The salmon oil was transesterified and then the FAMEs content was estimated using the peak areas of several FAMEs peaks observed in the chromatogram using GC-MS/FID analysis. The experimental results were modelled through Aspen Plus software, where the salmon oil was reacted with methanol in the presence of a catalyst and the results were validated against the experimental data. This study demonstrated that the oil produced from salmon wastes showed suitable characteristics and is a promising source for sustainable biofuel production.

Waste oils are considered a promising and sustainable source of raw materials in the biofuel industry. In recent years, research has been focused on the conversion of free fatty acids (FFAs) in fish oil to fatty acid methyl esters (FAMEs) mainly through the transesterification reaction. The biodiesel produced from salmon wastes provides an alternative source of raw material for sustainable energy production, meeting the growing energy demand without damaging the environment. The aim of this work is to investigate biodiesel production from salmon oil through a complete experimental design of the transesterification process. The biodiesel production was carried out by a homogeneous alkaline transesterification process using methanol and potassium methylate as a catalyst. The salmon oil was transesterified and then the FAMEs content was estimated using the peak areas of several FAMEs peaks observed in the chromatogram using GC-MS/FID analysis. The experimental results were modelled through Aspen Plus software, where the salmon oil was reacted with methanol in the presence of a catalyst and the results were validated against the experimental data. This study demonstrated that the oil produced from salmon wastes showed suitable characteristics and is a promising source for sustainable biofuel production.

Hard learning problems are important building blocks for the design of various cryptographic functionalities such as authentication protocols and post-quantum public key encryption. The standard implementations of such schemes add some controlled errors to simple (e.g., inner product) computations involving a public challenge and a secret key. Hard physical learning problems formalize the potential gains that could be obtained by leveraging inexact computing to directly generate erroneous samples. While they have good potential for improving the performances and physical security of more conventional samplers when implemented in specialized integrated circuits, it remains unknown whether physical defaults that inevitably occur in their instantiation can lead to security losses, nor whether their implementation can be viable on standard platforms such as FPGAs. We contribute to these questions in the context of the Learning Parity with Physical Noise (LPPN) problem by: (1) exhibiting new (output) data dependencies of the error probabilities that LPPN samples may suffer from; (2) formally showing that LPPN instances with such dependencies are as hard as the standard LPN problem; (3) analyzing an FPGA prototype of LPPN processor that satisfies basic security and performance requirements.

Noise in digital circuits has always been minimized to achieve high signal integrity, robust operation and of course high performance. However, for cryptographic applications, increased noise can in fact be beneficial. It can be used effectively to reduce the SNR and to make it harder for an adversary to extract useful information (secret keys) from the side channel leakage data. A natural question concerns the extent to which intrinsic MOSFET noise is required to improve security. In this manuscript, we explore this question and suggest ways to exploit the physical noise as a building block on which system level noise countermeasures can be further applied. The goal of this dissertation work is to quantify the intrinsic noise inherently present in a system and explore its manifestations through which it may be analyzed in a cryptographic context. This work could aid cryptographic designers in analyzing the intrinsic security of design implementations early in the development cycle, especially during design-stage evaluations. For research and academia, our work can be extended to explore different physical noise sources coupled to obtain a more holistic and consolidated view of the intrinsic security of design implementations., (FSA - Sciences de l'ingénieur) -- UCL, 2021

Learning parity with physical noise (LPPN) has been proposed as an assumption on which to build authentication protocols based on the learning parity with noise (LPN) problem. Its first advantage is to reduce the randomness requirements of standard LPN-based protocols, by directly performing erroneous computations so that no (e.g. Bernoulli-distributed) errors have to be generated on chip. At ASHES 2018, an LPPN processor was presented and confirmed the possibility to efficiently generate erroneous computations with the appropriate error rate. Since LPPN computations are key-homomorphic, they are good candidates for improved side-channel security thanks to masking, since they could theoretically lead to masked implementations with overheads that are linear in the number of shares, the analysis of which was left as an open problem. In this paper, we confirm this good potential by analyzing the side-channel security of an LPPN processor. We (1) evaluate the leakage of different parts of the erroneous computations, (2) conclude that intermediate computations that can be targeted with a divide-and-conquer Gaussian template attack are a sweet spot for side-channel attacks, and (3) showthat LPPN computations naturally reach a level of noise that makes masking effective, despite further noise addition could be beneficial to reach higher security at lower implementation cost.

Hard learning problems have recently attracted significant attention within the cryptographic community, both as a versatile assumption on which to build various protocols, and as a potentially sound basis for lightweight (possibly side-channel and fault resistant) implementations. Yet, in this second case, a recurrent drawback of primitives based on the Learning Parity with Noise and Learning With Errors problems is their additional randomness requirements to generate noise or errors. In parallel, the move towards nanoscale devices renders modern implementations increasingly prone to various types of errors. As a result, inexact computing has emerged as a new paradigm to efficiently deal with the challenges raised by such erroneous computations, and mitigate the cost and power consumption overheads they cause. In this paper, we show that these cryptographic and electronic challenges can actually be turned into new opportunities, and provide an elegant solution one to the other. That is, we show that inexact implementations of inner product computations lead to a natural way to define new Learning with Physical Noise or Error assumptions, paving the way to more efficient and physically secure implementations, with potential interest for securing emerging Internet of Things applications.

Hard learning problems have recently attracted significant attention within the cryptographic community, both as a versatile assumption on which to build various protocols, and as a potentially sound basis for lightweight (possibly side-channel and fault resistant) implementations. Yet, in this second case, a recurrent drawback of primitives based on the Learning Parity with Noise and Learning With Errors problems is their additional randomness requirements to generate noise or errors. In parallel, the move towards nanoscale devices renders modern implementations increasingly prone to various types of errors. As a result, inexact computing has emerged as a new paradigm to efficiently deal with the challenges raised by such erroneous computations, and mitigate the cost and power consumption overheads they cause. In this paper, we show that these cryptographic and electronic challenges can actually be turned into new opportunities, and provide an elegant solution one to the other. That is, we show that inexact implementations of inner product computations lead to a natural way to define new Learning with Physical Noise or Error assumptions, paving the way to more efficient and physically secure implementations, with potential interest for securing emerging Internet of Things applications.