Algorithmes quantiques pour la cryptanalyse et cryptographie symétrique post-quantique

Modern cryptography relies on the notion of computational security. The level of security given by a cryptosystem is expressed as an amount of computational resources required to break it. The goal of cryptanalysis is to find attacks, that is, algorithms with lower complexities than the conjectural bounds.With the advent of quantum computing devices, these levels of security have to be updated to take a whole new notion of algorithms into account. At the same time, cryptography is becoming widely used in small devices (smart cards, sensors), with new cost constraints.In this thesis, we study the security of secret-key cryptosystems against quantum adversaries.We first build new quantum algorithms for k-list (k-XOR or k-SUM) problems, by composing exhaustive search procedures. Next, we present dedicated cryptanalysis results, starting with a new quantum cryptanalysis tool, the offline Simon's algorithm. We describe new attacks against the lightweight algorithms Spook and Gimli and we perform the first quantum security analysis of the standard cipher AES.Finally, we specify Saturnin, a family of lightweight cryptosystems oriented towards post-quantum security. Thanks to a very similar structure, its security relies largely on the analysis of AES.La cryptographie moderne est fondée sur la notion de sécurité computationnelle. Les niveaux de sécurité attendus des cryptosystèmes sont exprimés en nombre d'opérations ; une attaque est un algorithme d'une complexité inférieure à la borne attendue. Mais ces niveaux de sécurité doivent aujourd'hui prendre en compte une nouvelle notion d'algorithme : le paradigme du calcul quantique. Dans le même temps,la délégation grandissante du chiffrement à des puces RFID, objets connectés ou matériels embarqués pose de nouvelles contraintes de coût.Dans cette thèse, nous étudions la sécurité des cryptosystèmes à clé secrète face à un adversaire quantique.Nous introduisons tout d'abord de nouveaux algorithmes quantiques pour les problèmes génériques de k-listes (k-XOR ou k-SUM), construits en composant des procédures de recherche exhaustive.Nous présentons ensuite des résultats de cryptanalyse dédiée, en commençant par un nouvel outil de cryptanalyse quantique, l'algorithme de Simon hors-ligne. Nous décrivons de nouvelles attaques contre les algorithmes Spook et Gimli et nous effectuons la première étude de sécurité quantique du chiffrement AES. Dans un troisième temps, nous spécifions Saturnin, une famille de cryptosystèmes à bas coût orientés vers la sécurité post-quantique. La structure de Saturnin est proche de celle de l'AES et sa sécurité en tire largement parti

Data-driven methods for statistical verification of uncertain nonlinear systems

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 277-290).Due to the increasing complexity of autonomous, adaptive, and nonlinear systems, engineers commonly rely upon statistical techniques to verify that the closed-loop system satisfies specified performance requirements at all possible operating conditions. However, these techniques require a large number of simulations or experiments to exhaustively search the set of possible parametric uncertainties for conditions that lead to failure. This work focuses on resource-constrained applications, such as preliminary control system design or experimental testing, which cannot rely upon exhaustive search to analyze the robustness of the closed-loop system to those requirements. This thesis develops novel statistical verification frameworks that combine data-driven statistical learning techniques and control system verification. First, two frameworks are introduced for verification of deterministic systems with binary and non-binary evaluations of each trajectory's robustness. These frameworks implement machine learning models to learn and predict the satisfaction of the requirements over the entire set of possible parameters from a small set of simulations or experiments. In order to maximize prediction accuracy, closed-loop verification techniques are developed to iteratively select parameter settings for subsequent tests according to their expected improvement of the predictions. Second, extensions of the deterministic verification frameworks redevelop these procedures for stochastic systems and these new stochastic frameworks achieve similar improvements. Lastly, the thesis details a method for transferring information between simulators or from simulators to experiments. Moreover, this method is introduced as part of a new failure-adverse closed-loop verification framework, which is shown to successfully minimize the number of failures during experimental verification without undue conservativeness. Ultimately, these data-driven verification frameworks provide principled approaches for efficient verification of nonlinear systems at all stages in the control system development cycle.by John Francis Quindlen.Ph. D