SAT-based preimage attacks on SHA-1

Hash functions are important cryptographic primitives which map arbitrarily long messages to fixed-length message digests in such a way that: (1) it is easy to compute the message digest given a message, while (2) inverting the hashing process (e.g. finding a message that maps to a specific message digest) is hard. One attack against a hash function is an algorithm that nevertheless manages to invert the hashing process. Hash functions are used in e.g. authentication, digital signatures, and key exchange. A popular hash function used in many practical application scenarios is the Secure Hash Algorithm (SHA-1). In this thesis we investigate the current state of the art in carrying out preimage attacks against SHA-1 using SAT solvers, and we attempt to find out if there is any room for improvement in either the encoding or the solving processes. We run a series of experiments using SAT solvers on encodings of reduced-difficulty versions of SHA-1. Each experiment tests one aspect of the encoding or solving process, such as e.g. determining whether there exists an optimal restart interval or determining which branching heuristic leads to the best average solving time. An important part of our work is to use statistically sound methods, i.e. hypothesis tests which take sample size and variation into account. Our most important result is a new encoding of 32-bit modular addition which significantly reduces the time it takes the SAT solver to find a solution compared to previously known encodings. Other results include the fact that reducing the absolute size of the search space by fixing bits of the message up to a certain point actually results in an instance that is harder for the SAT solver to solve. We have also identified some slight improvements to the parameters used by the heuristics of the solver MiniSat; for example, contrary to assertions made in the literature, we find that using longer restart intervals improves the running time of the solver

Computer Science for Continuous Data:Survey, Vision, Theory, and Practice of a Computer Analysis System

Building on George Boole's work, Logic provides a rigorous foundation for the powerful tools in Computer Science that underlie nowadays ubiquitous processing of discrete data, such as strings or graphs. Concerning continuous data, already Alan Turing had applied "his" machines to formalize and study the processing of real numbers: an aspect of his oeuvre that we transform from theory to practice.The present essay surveys the state of the art and envisions the future of Computer Science for continuous data: natively, beyond brute-force discretization, based on and guided by and extending classical discrete Computer Science, as bridge between Pure and Applied Mathematics