Lyra: Password-Based Key Derivation with Tunable Memory and Processing Costs

We present Lyra, a password-based key derivation scheme based on cryptographic sponges. Lyra was designed to be strictly sequential (i.e., not easily parallelizable), providing strong security even against attackers that use multiple processing cores (e.g., custom hardware or a powerful GPU). At the same time, it is very simple to implement in software and allows legitimate users to fine-tune its memory and processing costs according to the desired level of security against brute force password guessing. We compare Lyra with similar-purpose state-of-the-art solutions, showing how our proposal provides a higher security level and overcomes limitations of existing schemes. Specfically, we show that if we fix Lyra\u27s total processing time t in a legitimate platform, the cost of a memory-free attack against the algorithm is exponential, while the best known result in the literature (namely, against the scrypt algorithm) is quadratic. In addition, for an identical same processing time, Lyra allows for a higher memory usage than its counterparts, further increasing the cost of brute force attacks

FPGAs in Bioinformatics: Implementation and Evaluation of Common Bioinformatics Algorithms in Reconfigurable Logic

Life. Much effort is taken to grant humanity a little insight in this fascinating and complex but fundamental topic. In order to understand the relations and to derive consequences humans have begun to sequence their genomes, i.e. to determine their DNA sequences to infer information, e.g. related to genetic diseases. The process of DNA sequencing as well as subsequent analysis presents a computational challenge for recent computing systems due to the large amounts of data alone. Runtimes of more than one day for analysis of simple datasets are common, even if the process is already run on a CPU cluster. This thesis shows how this general problem in the area of bioinformatics can be tackled with reconfigurable hardware, especially FPGAs. Three compute intensive problems are highlighted: sequence alignment, SNP interaction analysis and genotype imputation. In the area of sequence alignment the software BLASTp for protein database searches is exemplarily presented, implemented and evaluated.SNP interaction analysis is presented with three applications performing an exhaustive search for interactions including the corresponding statistical tests: BOOST, iLOCi and the mutual information measurement. All applications are implemented in FPGA-hardware and evaluated, resulting in an impressive speedup of more than in three orders of magnitude when compared to standard computers. The last topic of genotype imputation presents a two-step process composed of the phasing step and the actual imputation step. The focus lies on the phasing step which is targeted by the SHAPEIT2 application. SHAPEIT2 is discussed with its underlying mathematical methods in detail, and finally implemented and evaluated. A remarkable speedup of 46 is reached here as well

FPGAs in der Bioinformatik: Implementierung und Evaluierung bekannter bioinformatischer Algorithmen in rekonfigurierbarer Logik

Life. Much effort is taken to grant humanity a little insight in this fascinating and complex but fundamental topic. In order to understand the relations and to derive consequences humans have begun to sequence their genomes, i.e. to determine their DNA sequences to infer information, e.g. related to genetic diseases. The process of DNA sequencing as well as subsequent analysis presents a computational challenge for recent computing systems due to the large amounts of data alone. Runtimes of more than one day for analysis of simple datasets are common, even if the process is already run on a CPU cluster. This thesis shows how this general problem in the area of bioinformatics can be tackled with reconfigurable hardware, especially FPGAs. Three compute intensive problems are highlighted: sequence alignment, SNP interaction analysis and genotype imputation. In the area of sequence alignment the software BLASTp for protein database searches is exemplarily presented, implemented and evaluated. SNP interaction analysis is presented with three applications performing an exhaustive search for interactions including the corresponding statistical tests: BOOST, iLOCi and the mutual information measurement. All applications are implemented in FPGA-hardware and evaluated, resulting in an impressive speedup of more than in three orders of magnitude when compared to standard computers. The last topic of genotype imputation presents a two-step process composed of the phasing step and the actual imputation step. The focus lies on the phasing step which is targeted by the SHAPEIT2 application. SHAPEIT2 is discussed with its underlying mathematical methods in detail, and finally implemented and evaluated. A remarkable speedup of 46 is reached here as well.Das Leben. Sehr viel Aufwand wird getrieben um der Menschheit einen Einblick in dieses faszinierende und komplexe, aber fundamentale Thema zu erlauben. Um Zusammenhänge zu verstehen und Folgen ableiten zu können hat der Mensch begonnen sein Genom zu sequenzieren, d.h. seine DNA zu bestimmen um daraus Informationen, z.B. in Bezug auf Erbkrankheiten folgern zu können. Der Prozess der DNA-Sequenzierung sowie die darauffolgenden Analysen sind schon allein wegen der riesigen Datenmengen eine Herausforderung für aktuelle Rechensysteme. Laufzeiten von über einen Tag für die Analyse einfacher Datensätze sind üblich, selbst wenn der Prozess bereits auf einem Computercluster ausgeführt wird. Diese Arbeit zeigt, wie dieses gängige Problem im Bereich der Bioinformatik mit rekonfigurierbarer Hardware, speziell FPGAs, angegangen werden kann. Es werden drei rechenintensive Themengebiete hervorgehoben: Sequenzalignment, SNP-Interaktionsanalyse und Genotyp-Imputation. Beispielhaft wird im Bereich des Sequenzalignments die Software BLASTp für die Suche in Proteinsequenzdatenbanken vorgestellt, implementiert und evaluiert. Die SNP-Interaktionsanalyse wird mit drei Verfahren zur vollständigen Suche von Interaktionen inklusive des dazugehörigen statistischen Tests vorgestellt: BOOST, iLOCi und die Messung der Transinformation. Alle Verfahren werden auf FPGA-Hardware implementiert und evaluiert, mit einer bestechenden Beschleunigung im dreistelligen Bereich gegenüber Standard-Rechnern. Das letzte Gebiet der Genotyp-Imputierung ist ein zweiteiliges Verfahren bestehend aus dem Phasing und der eigentlichen Imputation. Der Schwerpunkt liegt im Phasing-Schritt, der mit dem SHAPEIT2-Tool adressiert wird. SHAPEIT2 wird ausführlich mit den zugrunde liegenden mathematischen Methoden diskutiert, und schließlich implementiert und evaluiert. Auch hier wird ein beachtlicher Speedup von 46 erreicht