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Item type:Publication, Access status: with files , LWE-based encryption schemes and their applications in privacy-friendly data aggregationWe construct new Learning With Errors-based encryption schemes in order to solve two problems: privacy-preserving data aggregation and privacy-friendly social media marketing.The former allows for the aggregation of privacy-sensitive data from multiple users without trusting the aggregator. Our schemes improve over the state-of-the-art both in terms of runtime and bandwidth e ciency. In particular, our decryption routine is about 150 times faster compared to previous results. Secondly, we propose the first known solution for privacy-friendly social media marketing. Our construction maintains strong security and privacy guarantees and provides cryptographic proofs of correctness.Publicationtype: Doctoral Thesis664 3499 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Mapping dynamic programming algorithms on graphics processing unitsAlignment is the fundamental operation used to compare biological sequences. It also serves to identify regions of similarity that are eventually consequences of structural, functional, or evolutionary relationships. Today, the processing of sequences from large DNA or protein databases is a big challenge. Graphics Processing Units (GPUs) are based on a highly parallel, many-core streaming architecture and can be used to tackle the processing of large biological data. In the thesis, progressive alignment methods and their parallel implemenation by modern GPUs are studied. It turns out that wavefront and matrix-matrix product techniques can cope best with the data dependencies and so are highly appropriate for the implementation on a GPU. The performance of these methods is analyzed and the method with the highest speed-up is used to realize the alignment stage in the well-known software package ClustalW. Similar studies are made for the hidden Markov model. General principles and guidelines for GPU programming of matrix-based algorithms are discussed.Publicationtype: Doctoral Thesis915 1235