Attainable Unconditional Security for Shared-Key Cryptosystems

Preserving the privacy of private communication is a fundamental concern of computing addressed by encryption. Information-theoretic reasoning models unconditional security where the strength of the results does not depend on computational hardness or unproven results. Usually the information leaked on the message by the ciphertext is used to measure the privacy of a communication, with perfect secrecy when the leakage is zero. However this is hard to achieve in practice. An alternative measure is the equivocation, intuitively the average number of message/key pairs that could have produced a given ciphertext. We show a theoretical bound on equivocation called max-equivocation and show that this generalizes perfect secrecy when achievable, and provides an alternative measure when perfect secrecy is not. We derive bounds for max-equivocation for symmetric encoder functions and show that max-equivocation is achievable when the entropy of the ciphertext is minimized. We show that max-equivocation easily accounts for key re-use scenarios, and that large keys relative to the message perform very poorly under equivocation. We study encoders under this new perspective, deriving results on their achievable maximal equivocation and showing that some popular approaches such as Latin squares are not optimal. We show how unicity attacks can be naturally modeled, and how breaking encoder symmetry improves equivocation. We present some algorithms for generating encryption functions that are practical and achieve 90-95% of the theoretical best, improving with larger message spaces

De computatione quantica

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 23rd International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 31 regular papers presented in this volume were carefully reviewed and selected from 98 submissions. The papers cover topics such as categorical models and logics; language theory, automata, and games; modal, spatial, and temporal logics; type theory and proof theory; concurrency theory and process calculi; rewriting theory; semantics of programming languages; program analysis, correctness, transformation, and verification; logics of programming; software specification and refinement; models of concurrent, reactive, stochastic, distributed, hybrid, and mobile systems; emerging models of computation; logical aspects of computational complexity; models of software security; and logical foundations of data bases.

Machine Learning, Emulation and Bayesian Dimension Reduction for Climate Change Projection

Climate change projection under different greenhouse gas and aerosol emission scenarios is crucial for informing societal adaptation and mitigation measures. This traditionally relies on computationally expensive global climate models (GCMs) run on decadal to centennial timescales. One of the goals of this thesis is in exploring machine learning models and emulators trained on the output of global climate models, that can assist in this endeavour by providing rapid estimations of the climate response. Two statistical models are developed, one of which emulates the global short-term climate response to an emissions perturbation and one which learns the mapping from the short-term climate response to the long-term climate response. Different perspectives are taken so that the short-term response is predicted with a probabilistic emulator which interpolates between known and unknown data points, while the global patterns of long-term response are predicted with machine learning methods. Both models are shown to accelerate climate change projections and also provide new insights into the main drivers of climate change through sensitivity analysis to different emission perturbations and by uncovering consistent early indicators of long-term climate response. Discovering structures in climate data that can explain patterns and behaviour is another focus of this thesis, addressed through a dimension reduction technique to simplify large datasets. This is approached from a Bayesian perspective which could allow a complete quantification of uncertainty when making predictions through an emulator trained on a reduced dataset. Reversible jump Markov chain Monte Carlo and Sequential Monte Carlo algorithms are developed for a latent factor model to infer the probability distribution on both the number of underlying dimensions and the structure of these. Sequential Monte Carlo is found to be significantly more effective at determining these and is demonstrated on weather observations to reveal underlying factors governing the weather behaviour