Pushing the speed limit of constant-time discrete Gaussian sampling. A case study on Falcon.

Sampling from discrete Gaussian distribution has applications in lattice-based post-quantum cryptography. Several efficient solutions have been proposed in recent years. However, making a Gaussian sampler secure against timing attacks turned out to be a challenging research problem. In this work, we observed an important property of the input random bit strings that generate samples in Knuth-Yao sampling. We delineate a generic step-by-step method to instantiate a discrete Gaussian sampler of arbitrary standard deviation and precision by efficiently minimizing the Boolean expressions by exploiting this prop- erty. Discrete Gaussian samplers generated in this method can be up to 37% faster than the state of the art method. Finally, we show that the signing algorithm of post-quantum signature scheme Falcon using our constant-time sampler is at most 33% slower than the fastest non-constant time sampler

Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections

Isochronous Gaussian Sampling: From Inception to Implementation

Gaussian sampling over the integers is a crucial tool in lattice-based cryptography, but has proven over the recent years to be surprisingly challenging to perform in a generic, efficient and provable secure manner. In this work, we present a modular framework for generating discrete Gaussians with arbitrary center and standard deviation. Our framework is extremely simple, and it is precisely this simplicity that allowed us to make it easy to implement, provably secure, portable, efficient, and provably resistant against timing attacks. Our sampler is a good candidate for any trapdoor sampling and it is actually the one that has been recently implemented in the Falcon signature scheme. Our second contribution aims at systematizing the detection of implementation errors in Gaussian samplers. We provide a statistical testing suite for discrete Gaussians called SAGA (Statistically Acceptable GAussian). In a nutshell, our two contributions take a step towards trustable and robust Gaussian sampling real-world implementations

cuFE: High Performance Privacy Preserving Support Vector Machine with Inner-Product Functional Encryption

Privacy preservation is a sensitive issue in our modern society. It is becoming increasingly important in many applications in this ever-growing and highly connected digital era. Functional encryption is a computation on encrypted data paradigm that allows users to retrieve the evaluation of a function on encrypted data without revealing the data, thus effectively protecting users\u27 privacy. However, existing functional encryption implementations are still very time-consuming for practical deployment, especially when applied to machine learning applications that involve a huge amount of data. In this paper, we present a high-performance implementation of inner-product functional encryption (IPFE) based on ring-learning with errors on graphics processing units. We propose novel techniques to parallelize the Gaussian sampling, which is one of the most time-consuming operations in the IPFE scheme. We further execute a systematic investigation to select the best strategy for implementing number theoretic transform and inverse number theoretic transform for different security levels. Compared to the existing AVX2 implementation of IPFE, our implementation on a RTX 2060 GPU device can achieve 34.24x, 40.02x, 156.30x, and 18.76x speed-up for Setup, Encrypt, KeyGen, and Decrypt respectively. Finally, we propose a fast privacy-preserving Support Vector Machine (SVM) application to classify data securely using our GPU-accelerated IPFE scheme. Experimental results show that our implementation can classify 100 inputs with 591 support vectors in 688 ms (less than a second), which is 33.12x faster than the AVX2 version which takes 23 seconds

Fundamentals and applications of spatial dissipative solitons in photonic devices : [Chapter 6]

We review the properties of optical spatial dissipative solitons (SDS). These are stable, self‐localized optical excitations sitting on a uniform, or quasi‐uniform, background in a dissipative environment like a nonlinear optical cavity. Indeed, in optics they are often termed “cavity solitons.” We discuss their dynamics and interactions in both ideal and imperfect systems, making comparison with experiments. SDS in lasers offer important advantages for applications. We review candidate schemes and the tremendous recent progress in semiconductor‐based cavity soliton lasers. We examine SDS in periodic structures, and we show how SDS can be quantitatively related to the locking of fronts. We conclude with an assessment of potential applications of SDS in photonics, arguing that best use of their particular features is made by exploiting their mobility, for example in all‐optical delay lines

Tangible Scalar Fields

Data Visualization is a field that explores how to most efficiently convey information to the user, most often via visual representations like plots, graphs or glyphs. While this field of research has had great growth within the last couple of years, most of the work has been focused on the visual part of the human visual and auditory system - much less visualization work has been done in regards to the visually impaired. In this thesis, we will look at some previous methods and techniques for visualizing scalar fields via the sense of touch, and additionally provide two novel approaches to visualize a two-dimensional scalar field. Our first approach creates passive physicalizations from a scalar field in a semi-automatic pipeline by encoding the scalar value and field coordinates as positions in 3D space, which we use to construct a triangular mesh built from hexagonal pillars that can be printed on a 3D printer. We further enhance our mesh by encoding a directional attribute on the pillars, creating a visual encoding of the model orientation and improving upon a readability issue by mirroring the mesh. Our second approach uses a haptic force-feedback device to simulate the feeling of moving across a surface based on the scalar field by replicating three physical forces: the normal force, the friction force and the gravity force. We also further extend our approach by introducing a local encoding of global information about the scalar field via a volume representation build from the scalar field.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Sécurité étendue de la cryptographie fondée sur les réseaux euclidiens

Lattice-based cryptography is considered as a quantum-safe alternative for the replacement of currently deployed schemes based on RSA and discrete logarithm on prime fields or elliptic curves. It offers strong theoretical security guarantees, a large array of achievable primitives, and a competitive level of efficiency. Nowadays, in the context of the NIST post-quantum standardization process, future standards may ultimately be chosen and several new lattice-based schemes are high-profile candidates. The cryptographic research has been encouraged to analyze lattice-based cryptosystems, with a particular focus on practical aspects. This thesis is rooted in this effort.In addition to black-box cryptanalysis with classical computing resources, we investigate the extended security of these new lattice-based cryptosystems, employing a broad spectrum of attack models, e.g. quantum, misuse, timing or physical attacks. Accounting that these models have already been applied to a large variety of pre-quantum asymmetric and symmetric schemes before, we concentrate our efforts on leveraging and addressing the new features introduced by lattice structures. Our contribution is twofold: defensive, i.e. countermeasures for implementations of lattice-based schemes and offensive, i.e. cryptanalysis.On the defensive side, in view of the numerous recent timing and physical attacks, we wear our designer’s hat and investigate algorithmic protections. We introduce some new algorithmic and mathematical tools to construct provable algorithmic countermeasures in order to systematically prevent all timing and physical attacks. We thus participate in the actual provable protection of the GLP, BLISS, qTesla and Falcon lattice-based signatures schemes.On the offensive side, we estimate the applicability and complexity of novel attacks leveraging the lack of perfect correctness introduced in certain lattice-based encryption schemes to improve their performance. We show that such a compromise may enable decryption failures attacks in a misuse or quantum model. We finally introduce an algorithmic cryptanalysis tool that assesses the security of the mathematical problem underlying lattice-based schemes when partial knowledge of the secret is available. The usefulness of this new framework is demonstrated with the improvement and automation of several known classical, decryption-failure, and side-channel attacks.La cryptographie fondée sur les réseaux euclidiens représente une alternative prometteuse à la cryptographie asymétrique utilisée actuellement, en raison de sa résistance présumée à un ordinateur quantique universel. Cette nouvelle famille de schémas asymétriques dispose de plusieurs atouts parmi lesquels de fortes garanties théoriques de sécurité, un large choix de primitives et, pour certains de ses représentants, des performances comparables aux standards actuels. Une campagne de standardisation post-quantique organisée par le NIST est en cours et plusieurs schémas utilisant des réseaux euclidiens font partie des favoris. La communauté scientifique a été encouragée à les analyser car ils pourraient à l’avenir être implantés dans tous nos systèmes. L’objectif de cette thèse est de contribuer à cet effort.Nous étudions la sécurité de ces nouveaux cryptosystèmes non seulement au sens de leur résistance à la cryptanalyse en “boîte noire” à l’aide de moyens de calcul classiques, mais aussi selon un spectre plus large de modèles de sécurité, comme les attaques quantiques, les attaques supposant des failles d’utilisation, ou encore les attaques par canaux auxiliaires. Ces différents types d’attaques ont déjà été largement formalisés et étudiés par le passé pour des schémas asymétriques et symétriques pré-quantiques. Dans ce mémoire, nous analysons leur application aux nouvelles structures induites par les réseaux euclidiens. Notre travail est divisé en deux parties complémentaires : les contremesures et les attaques.La première partie regroupe nos contributions à l’effort actuel de conception de nouvelles protections algorithmiques afin de répondre aux nombreuses publications récentes d’attaques par canaux auxiliaires. Les travaux réalisés en équipe auxquels nous avons pris part on abouti à l’introduction de nouveaux outils mathématiques pour construire des contre-mesures algorithmiques, appuyées sur des preuves formelles, qui permettent de prévenir systématiquement les attaques physiques et par analyse de temps d’exécution. Nous avons ainsi participé à la protection de plusieurs schémas de signature fondés sur les réseaux euclidiens comme GLP, BLISS, qTesla ou encore Falcon.Dans une seconde partie consacrée à la cryptanalyse, nous étudions dans un premier temps de nouvelles attaques qui tirent parti du fait que certains schémas de chiffrement à clé publique ou d’établissement de clé peuvent échouer avec une faible probabilité. Ces échecs sont effectivement faiblement corrélés au secret. Notre travail a permis d’exhiber des attaques dites « par échec de déchiffrement » dans des modèles de failles d’utilisation ou des modèles quantiques. Nous avons d’autre part introduit un outil algorithmique de cryptanalyse permettant d’estimer la sécurité du problème mathématique sous-jacent lorsqu’une information partielle sur le secret est donnée. Cet outil s’est avéré utile pour automatiser et améliorer plusieurs attaques connues comme des attaques par échec de déchiffrement, des attaques classiques ou encore des attaques par canaux auxiliaires

Fast, Three-Dimensional Fluorescence Imaging of Living Cells

This thesis focuses on multi-plane fluorescence microscopy for fast live-cell imaging. To improve the performance of multi-plane microscopy, I developed new image analysis methods. I used these methods to measure and analyze the movements of cardiomyocytesand Dictyostelium discoideum cells.The multi-plane setup is based on a conventional wide-field microscope using a custom multiple beam-splitter in the detection path. This prism creates separate images of eight distinct focal planes in the sample. Since 3D volume is imaged without scanning, three-dimensional imaging at a very high speed becomes possible. However, as in conventional wide-field microscopy, the "missing cone" of spatial frequencies along the optical axis in the optical transfer function (OTF) prevents optical sectioning in such a microscope. This is in stark contrast to other truly three-dimensional imaging modalities like confocal and light-sheet microscopy. In order to overcome the lack of optical sectioning, I developed a new deconvolution method. Deconvolution describes methods that restore or sharpen an image based on physical assumptions and knowledge of the imaging process. Deconvolution methods have been widely used to sharpen images of microscopes and telescopes. The recently developed SUPPOSe algorithm is a deconvolution algorithm that uses a set of numerous virtual point sources. It tries to reconstruct an image by distributing these point sources in space and optimizing their positions so that the resulting image reproduces as good as possible the measured data. SUPPOSe has never been used for 3D images. Compared to other algorithms, this method has superior performance when the number of pixels is increased by interpolation. In this work, I extended the method to work also with 3D image data. The 3D-SUPPOSe program is suitable for analyzing data of our multi-plane setup. The multi-plane setup has only eight vertically aligned image planes. Furthermore, for accurate reconstruction of 3D images, I studied a method of correcting each image plane's relative brightness constituting an image, and I also developed a method of measuring the movement of point emitters in 3D space. Using these methods, I measured and analyzed the beating motion of cardiomyocytes and the chemotaxis of Dicyosteilium discoidem. Cardiomyocytes are the cells of the heart muscle and consist of repetitive sarcomeres. These cells are characterized by fast and periodic movements, and so far the dynamics of these cells was studied only with two-dimensional imaging. In this thesis, the beating motion was analyzed by tracing the spatial distribution of the so-called z-discs, one of the constituent components of cardiomyocytes. I found that the vertical distribution of $\alpha$-actinine-2 in a single z-disc changed very rapidly, which may serve as a starting point for a better understanding the motion of cardiomyocytes. \textit{Dictyostelium discoideum} is a well established single cell model organism that migrates along the gradient of a chemoattractant. One has conducted much research to understand the mechanism of chemotaxis, and many efforts have been made to understand the role of actin in the chemotactic motion. By suppressing the motor protein, myosin, a cell line was created that prevented the formation of normal actin filaments. In these myosin null cells, F-actin moves in a flow-like behaviour and induces cell movement. In this study, I imaged the actin dynamics, and I analyzed the flow using the newly created deconvolution and flow estimation methods. As a result of the analysis, the spatio-temporal correlation between pseudo-pod formation and dynamics and actin flow was investigated.2022-01-2

Multitemporal assessment of crop parameters using multisensorial flying platforms

UAV sensors suitable for precision farming (Sony NEX-5n RGB camera; Canon Powershot modified to infrared sensitivity; MCA6 Tetracam; UAV spectrometer) were compared over differently treated grassland. The high resolution infrared and RGB camera allows spatial analysis of vegetation cover while the UAV spectrometer enables detailed analysis of spectral reflectance at single points. The high spatial and six-band spectral resolution of the MCA6 combines the opportunities of spatial and spectral analysis, but requires huge calibration efforts to acquire reliable data. All investigated systems were able to provide useful information in different distinct research areas of interest in the spatial or spectral domain. The UAV spectrometer was further used to assess multiangular reflectance patterns of wheat. By flying the UAV in a hemispherical path and directing the spectrometer towards the center of this hemisphere, the system acts like a large goniometer. Other than ground based goniometers, this novel method allows huge diameters without any need for infrastructures on the ground. Our experimental results shows good agreement with models and other goniometers, proving the approach valid. UAVs are capable of providing airborne data with a high spatial and temporal resolution due to their flexible and easy use. This was demonstrated in a two year survey. A high resolution RGB camera was flown every week over experimental plots of barley. From the RGB imagery a time series of the barley development was created using the color values. From this analysis we could track differences in the growth of multiple seeding densities and identify events of plant development such as ear pushing. These results lead towards promising practical applications that could be used in breeding for the phenotyping of crop varieties or in the scope of precision farming. With the advent of high endurance UAVs such as airships and the development of better light weight sensors, an exciting future for remote sensing from UAV in agriculture is expected